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Asset Rotation System [InvestorUnknown]Overview This system creates a comprehensive trend "matrix" by analyzing the performance of six assets against both the US Dollar and each other. The objective is to identify and hold the asset that is currently outperforming all others, thereby focusing on maintaining an investment in the most "optimal" asset at any given time. - - - Key Features - - - 1. Trend Classification: The system evaluates the trend for each of the six assets, both individually against USD and in pairs (assetX/assetY), to determine which asset is currently outperforming others. Utilizes five distinct trend indicators: RSI (50 crossover), CCI, SuperTrend, DMI, and Parabolic SAR. Users can customize the trend analysis by selecting all indicators or choosing a single one via the "Trend Classification Method" input setting. 2. Backtesting: Calculates an equity curve for each asset and for the system itself, which assumes holding only the asset deemed optimal at any time. Customizable start date for backtesting; by default, it begins either 5000 bars ago (the maximum in TradingView) or at the inception of the youngest asset included, whichever is shorter. If the youngest asset's history exceeds 5000 bars, the system uses 5000 bars to prevent errors. The equity curve is dynamically colored based on the asset held at each point, with this coloring also reflected on the chart via barcolor(). Performance metrics like returns, standard deviation of returns, Sharpe, Sortino, and Omega ratios, along with maximum drawdown, are computed for each asset and the system's equity curve. 3 Alerts: Supports alerts for when a new, confirmed optimal asset is identified. However, due to TradingView limitations, the specific asset cannot be included in the alert message. - - - Usage - - - 1. Select Assets/Tickers: Choose which assets or tickers you want to include in the rotation system. Ensure that all selected tickers are denominated in USD to maintain consistency in analysis. 2. Configure Trend Classification: Decide on the trend classification method from the available options (RSI, CCI, SuperTrend, DMI, or Parabolic SAR, All) and adjust the settings to your preferences. This customization allows you to tailor the system to different market conditions or your specific trading strategy. 3. Utilize Backtesting for Calibration: Use the backtesting results, including equity curves and performance metrics, to fine-tune your chosen trend indicators. Be cautious not to overemphasize performance maximization, as this can lead to overfitting. The goal is to achieve a robust system that performs well across various market conditions, rather than just optimizing for past data. - - - Parameters - - - Tickers: Asset 1: Select the symbol for the first asset. Asset 2: Select the symbol for the second asset. Asset 3: Select the symbol for the third asset. Asset 4: Select the symbol for the fourth asset. Asset 5: Select the symbol for the fifth asset. Asset 6: Select the symbol for the sixth asset. General Settings: Trend Classification Method: Choose from RSI, CCI, SuperTrend, DMI, PSAR, or "All" to determine how trends are analyzed. Use Custom Starting Date for Backtest: Toggle to use a custom date for beginning the backtest. Custom Starting Date: Set the custom start date for backtesting. Plot Perf. Metrics Table: Option to display performance metrics in a table on the chart. RSI (Relative Strength Index): RSI Source: Choose the price data source for RSI calculation. RSI Length: Set the period for the RSI calculation. CCI (Commodity Channel Index): CCI Source: Select the price data source for CCI calculation. CCI Length: Determine the period for the CCI. SuperTrend: SuperTrend Factor: Adjust the sensitivity of the SuperTrend indicator. SuperTrend Length: Set the period for the SuperTrend calculation. DMI (Directional Movement Index): DMI Length: Define the period for DMI calculations. Parabolic SAR: PSAR Start: Initial acceleration factor for the Parabolic SAR. PSAR Increment: Increment value for the acceleration factor. PSAR Max Value: Maximum value the acceleration factor can reach. Notes/Recommendations: While this system is operational, it's important to recognize that it relies on "basic" indicators, which may not be ideal for generating trading signals on their own. I strongly suggest that users delve into the code to grasp the underlying logic of the system. Consider customizing it by integrating more sophisticated and higher-quality trend-following indicators to enhance its performance and reliability. Disclaimer: This system's backtest results are historical and do not predict future performance. Use for educational purposes only; not investment advice.
Indicador Pine Script®
por InvestorUnknown
27
Smart DCA Strategy (Public)INSPIRATION While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness. The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy. WHY IS IT SMART? The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA. WHAT THIS STRATEGY IS, AND IS NOT This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used. You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed. HOW IT WORKS The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading. HOW TO USE Settings: The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap. The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results. Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets. In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered. You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings. Backtesting: When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading. The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost. Live Trading: After you've finished backtesting you can proceed with configuring your alerts for live trading. But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on BTCUSD Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day Enter that USD amount in the Blind buy size settings box Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period. After you have calculated the Smart buy size, you can go ahead and set up alerts on Smart DCA buy triggers. BOT AUTOMATION In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator. PLANNED FUTURE DEVELOPMENTS Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space! MARKETS Crypto: This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets. Stocks: I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish. Forex: As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex. For more info about this strategy including backtest results, please see the full description on the invite only version of this strategy named "Smart DCA Strategy"
Indicador Pine Script®
por kevinmck100
2
FirstStrike Long 200 - Daily Trend Rider [KedArc Quant]Strategy Description FirstStrike Long 200 is a disciplined, long-only momentum strategy designed for daily "strike-first" entries in trending markets. It scans for RSI momentum above a customizable trigger (default 50), confirmed by EMA trend filters, and limits you to *exactly one trade per day* to avoid overtrading. It uses ATR for dynamic risk management (1.5x stop, 2:1 RR target) and optional trailing stops to ride winners. Backtested with realistic commissions and sizing, it prioritizes low drawdowns (<1% max in tests) over aggressive gains—ideal for swing traders seeking quality setups in bull runs. Why It's Different from Other Strategies Unlike generic RSI crossover bots or EMA ribbon mashups that spam signals and bleed in chop, FirstStrike enforces a "one-and-done" daily gate, blending precision momentum (RSI modes with grace/sustain) with robust filters (volume, sessions, rearm dips). How It Helps Traders - Reduces Emotional Trading: One entry/day forces discipline—miss a setup? Wait for tomorrow. Perfect for busy pros avoiding screen fatigue. - Adapts to Regimes: Switch modes for trends ("Cross+Grace") vs. ranges ("Any bar")—boosts win rates 5-10% in backtests on high-beta names like . - Risk-First Design: ATR scales stops to vol capping DD at 0.2% while targeting 2R winners. Trailing option locks +3-5% runs without early exits. - Quick Insights: Labels/alerts flag entries with RSI values; bgcolor highlights signals for visual scanning. Helps spot "first-strike" edges in uptrends, filtering ~60% noise. Why This Is Not a Mashup This isn't a Frankenstein of off-the-shelf indicators—while it uses standard RSI/EMA/ATR (core Pine primitives), the innovation lies in: - Custom Trigger Engine: Switchable modes (e.g., "Cross+Grace+Sustain" requires post-cross hold) prevent perpetual signals, unlike basic `ta.crossover()`. - Daily Rearm Gate: Resets eligibility only after a dip (if enabled), tying momentum to mean-reversion—original logic not found in common scripts. - Per-Day Isolation: `var` vars + `ta.change(time("D"))` ensure zero pyramiding/overlaps, beyond simple session filters. All formulae are derived in-house for "first-strike" (early RSI pops in trends), not copied from public repos. Input Configurations Let's break down every input in the FirstStrike Long 200 strategy. These settings let you tweak the strategy like a dashboard—start with defaults for quick testing, then adjust based on your asset or timeframe (5m for intraday). They're grouped logically to keep things organized, and most have tooltips in the script for quick reminders. RSI / Trigger Group: The Heart of Momentum Detection This is where the magic starts—the strategy hunts for "upward energy" using RSI (Relative Strength Index), a tool that measures if a stock is overbought (too hot) or oversold (too cold) on a 0-100 scale. - RSI Length: How many bars (candles) back to calculate RSI. Default is 14, like a 14-day window for daily charts. Shorter (e.g., 9) makes it snappier for fast markets; longer (21) smooths out noise but misses quick turns. - Trigger Level (RSI >= this): The key RSI value where the strategy says, "Go time!" Default 50 means enter when RSI crosses or holds above the neutral midline. Why is this trigger required? It acts as your "green light" filter—without it, you'd enter on every tiny price wiggle, leading to endless losers. RSI above this shows building buyer power, avoiding weak or sideways moves. It's essential for quality over quantity, especially in one-trade-per-day setups. - Trigger Mode: Picks how strict the RSI signal must be. Options: "Cross only" (exact RSI crossover above trigger—super precise, fewer trades); "Cross+Grace" (crossover or within a grace window after—gives a second chance); "Cross+Grace+Sustain" (crossover/grace plus RSI holding steady for bars—best for steady climbs); "Any bar >= trigger" (looser, any bar above—more opportunities but riskier in chop). Start with "Any bar" for trends, switch to "Cross only" for caution. - Grace Window (bars after cross): If mode allows, how many bars post-RSI-cross you can still enter if RSI dips but recovers. Default 30 (about 2.5 hours on 5m). Zero means no wiggle room—pure precision. - Sustain Bars (RSI >= trigger): In sustain mode, how many straight bars RSI must stay above trigger. Default 3 ensures it's not a fluke spike. - Require RSI Dip Below Rearm Before Any Entry?: A yes/no toggle. If on, the strategy "rearms" only after RSI dips below a low level (like a breather), preventing back-to-back signals in overextended rallies. - Rearm Level (if requireDip=true): The dip threshold for rearming. Default 45—RSI must go below this to reset eligibility. Lower (30) for deeper pullbacks in volatile stocks. For the trigger level itself, presets matter a lot—default 50 is neutral and versatile for broad trends. Bump to 55-60 for "strong momentum only" (fewer but higher-win trades, great in bull runs like tech surges); drop to 40-45 for "early bird" catches in recoveries (more signals but watch for fakes in ranges). The optimize hint (40-60) lets you test these in TradingView to match your risk—higher presets cut noise by 20-30% in backtests. Trend / Filters Group: Keeping You on the Right Side of the Market These EMAs (Exponential Moving Averages) act like guardrails, ensuring you only long in uptrends. - EMA (Fast) Confirmation: Short-term EMA for price action. Default 20 periods—price must be above this for "recent strength." Shorter (10) reacts faster to intraday pops. - EMA (Trend Filter): Long-term EMA for big-picture trend. Default 200 (classic "above the 200-day" rule)—price above it confirms bull market. Minimum 50 to avoid over-smoothing. Optional Hour Window Group: Timing Your Strikes Avoid bad hours like lunch lulls or after-hours tricks. - Restrict by Session?: Yes/no for using exact market hours. Default off. - Session (e.g., 0930-1600 for NYSE): Time string like "0930-1600" for open to close. Auto-skips pre/post-market noise. - Restrict by Hour Range?: Fallback yes/no for simple hours. Default off. - Start Hour / End Hour: Clock times (0-23). Defaults 9-15 ET—focus on peak volume. Volume Filter Group: No Volume, No Party Confirms conviction—big moves need big participation. - Require Volume > SMA?: Yes/no toggle. Default off—only fires on above-average volume. - Volume SMA Length: Periods for the average. Default 20—compares current bar to recent norm. Risk / Exits Group: Protecting and Profiting Smartly Dynamic stops based on volatility (ATR = Average True Range) keep things realistic. - ATR Length: Bars for ATR calc. Default 14—measures recent "wiggle room" in price. - ATR Stop Multiplier: How far below entry for stop-loss. Default 1.5x ATR—gives breathing space without huge risk - Take-Profit R Multiple: Reward target as multiple of risk. Default 2.0 (2:1 ratio)—aims for twice your stop distance. - Use Trailing Stop?: Yes/no for profit-locking trail. Default off—activates after entry. - Trailing ATR Multiplier: Trail distance. Default 2.0x ATR—looser than initial stop to let winners run. These inputs make the strategy plug-and-play: Defaults work out-of-box for trending stocks, but tweak RSI trigger/modes first for your style. Always backtest changes—small shifts can flip a 40% win rate to 50%+! Outputs (Visuals & Alerts): - Plots: Blue EMA200 (trend line), Orange EMA20 (price filter), Green dashed entry price. - Labels: Green "LONG" arrow with RSI value on entries. - Background: Light green highlight on signal bars. - Alerts: "FirstStrike Long Entry" fires on conditions (integrates with TradingView notifications). Entry-Exit Logic Entry (Long Only, One Per Day): 1. Daily Reset: New day clears trade gate and (if required) rearm status. 2. Filters Pass: Time/session OK + Close > EMA200 (trend) + Close > EMA20 (price) + Volume > SMA (if enabled) + Rearmed (dip below rearm if toggled). 3. Trigger Fires: RSI >= trigger via selected mode (e.g., crossover + grace window). 4. Execute: Enter long at close; set daily flag to block repeats. Exit: - Stop-Loss: Entry - (ATR * 1.5) – dynamic, vol-scaled. - Take-Profit: Entry + (Risk * 2.0) – fixed RR. - Trailing (Optional): Activates post-entry; trails at Close - (ATR * 2.0), updating on each bar for trend extension. No shorts or hedging—pure long bias. Formulae Used - RSI: `ta.rsi(close, rsiLen)` – Standard 14-period momentum oscillator (0-100). - EMAs: `ta.ema(close, len)` – Exponential moving averages for trend/price filters. - ATR: `ta.atr(atrLen)` – True range average for stop sizing: Stop = Entry - (ATR * mult). - Volume SMA: `ta.sma(volume, volLen)` – Simple average for relative strength filter. - Grace Window: `bar_index - lastCrossBarIndex <= graceBars` – Counts bars since RSI crossover. - Sustain: `ta.barssince(rsi < trigger) >= sustainBars` – Consecutive bars above threshold. - Session Check: `time(timeframe.period, sessionStr) != 0` – TradingView's built-in session validator. - Risk Distance: `riskPS = entry - stop; TP = entry + (riskPS * RR)` – Asymmetric reward calc. FAQ Q: Why only one trade/day? A: Prevents revenge trading in volatile sessions . Backtests show it cuts losers by 20-30% vs. multi-entry bots. Q: Does it work on all assets/timeframes? A: Best for trending stocks/indices on 5m-1H. Test on crypto/forex with wider ATR mult (2.0+). Q: How to optimize? A: Use TradingView's optimizer on RSI trigger (40-60) and EMA fast (10-30). Aim for PF >1.0 over 1Y data. Q: Alerts don't fire—why? A: Ensure `alertcondition` is enabled in script settings. Test with "Any alert() function calls only." Q: Trailing stop too loose? A: Tune `trailMult` to 1.5 for tighter; it activates alongside fixed TP/SL for hybrid protection. Glossary - Grace Window: Post-RSI-cross period (bars) where entry still allowed if RSI holds trigger. - Rearm Dip: Optional pullback below a low RSI level (e.g., 45) to "reset" eligibility after signals. - Profit Factor (PF): Gross profit / gross loss—>1.0 means winners outweigh losers. - R Multiple: Risk units (e.g., 2R = 2x stop distance as target). - Sustain Bars: Consecutive bars RSI stays >= trigger for mode confirmation. Recommendations - Backtest First: Run on your symbols (/) over 6-12M; tweak RSI to 55 for +5% win rate. - Live Use: Start paper trading with `useSession=true` and `useVol=true` to filter noise. - Pairs Well With: Higher TF (daily) for bias; add ADX (>25) filter for strong trends (code snippet in prior chats). - Risk Note: 10% sizing suits $100k+ accounts; scale down for smaller. Not financial advice—past performance ≠ future. - Publish Tip: Add tags like "momentum," "RSI," "long-only" on TradingView for visibility. Strategy Properties & Backtesting Setup FirstStrike Long 200 is configured with conservative, realistic backtesting parameters to ensure reliable performance simulations. These settings prioritize capital preservation and transparency, making it suitable for both novice and experienced traders testing on stocks. Initial Capital $100,000 Standard starting equity for portfolio-level testing; scales well for retail accounts. Adjust lower (e.g., $10k) for smaller simulations. Base Currency Default (USD) Aligns with most US equities (e.g., NASDAQ symbols); auto-converts for other assets. Order Size 1 (Quantity) Fixed share contracts for simplicity—e.g., buys 1 share per trade. For % of equity, switch to "Percent of Equity" in strategy code. Pyramiding 0 Orders No additional entries on open positions; enforces strict one-trade-per-day discipline to avoid overexposure. Commission 0.1% Realistic broker fee (e.g., Interactive Brokers tier); factors in round-trip costs without over-penalizing winners. Verify Price for Limit Orders 0 Ticks No slippage delay on TPs—assumes ideal fills for historical accuracy. Slippage 0 Ticks Zero assumed slippage for clean backtests; real-world trading may add 1-2 ticks on volatile opens. These defaults yield low drawdowns (<0.3% max in tests) while capturing trend edges. For live trading, enable slippage (1-3 ticks) to mimic execution gaps. Always forward-test before deploying! ⚠️ Disclaimer This script is provided for educational purposes only. Past performance does not guarantee future results. Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
Estrategia Pine Script®
por kedarcquant
Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail. 1. General Description Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions. Key Features: Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes. Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses. Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes. Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold). Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle). Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results). Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio. Strategy Settings: Initial capital: $10,000. Pyramiding: Up to 3 positions. Position size: 10% of equity (default_qty_value=10). Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management. Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading. 2. Working Logic (Step by Step) The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works: a. Input Parameters The inputs are grouped for clarity: Herd Behavior (🐑): RSI Period (14): For overbought/oversold detection. Volume MA Period (20): To calculate average volume for spike detection. Herd Threshold (2.0): Volume multiplier for detecting herd activity. Liquidity Analysis (💧): Liquidity Lookback (50): Bars to check for recent highs/lows. Liquidity Sensitivity (1.5): Volume multiplier for trap detection. Institutional Flow (🏦): Institutional Volume Multiplier (2.5): For detecting large volume spikes. Institutional MA Period (21): For Accumulation/Distribution smoothing. Nash Equilibrium (⚖️): Nash Period (100): For calculating price mean and standard deviation. Nash Deviation (0.02): Multiplier for equilibrium bands. Risk Management (🛡️): Use Stop-Loss (true): Enables SL at 2% below/above entry price. Use Take-Profit (true): Enables TP at 5% above/below entry price. b. Herd Behavior Detection RSI (14): Checks for extreme conditions: Overbought: RSI > 70 (potential herd buying). Oversold: RSI < 30 (potential herd selling). Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold). Momentum: Price change over 10 bars (close - close ) compared to its SMA(20). Herd Signals: Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background). Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background). c. Liquidity Trap Detection Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback). Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones. Trap Conditions: Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5). Down Trap: Price breaks recent low, closes above it, with a volume spike. Visualization: Traps are marked with small red/green crosses above/below bars. d. Institutional Flow Analysis Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity. Accumulation/Distribution (AD): Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time. Smoothed with SMA(21) (inst_ma_length). Accumulation: AD > MA + high volume = Institutions buying. Distribution: AD < MA + high volume = Institutions selling. Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying. e. Nash Equilibrium Calculation: Price mean: SMA(100) (nash_period). Standard deviation: stdev(100). Upper Nash: Mean + StdDev x 0.02 (nash_deviation). Lower Nash: Mean - StdDev x 0.02. Conditions: Near Equilibrium: Price between upper and lower Nash bands (stable market). Above Nash: Price > upper band (overbought, sell potential). Below Nash: Price < lower band (oversold, buy potential). Visualization: Orange line (mean), red/green lines (upper/lower bands). f. Game Theory Signals The strategy generates three types of signals, combined into long/short triggers: Contrarian Signals: Buy: Herd selling + (accumulation or down trap) = Go against retail panic. Sell: Herd buying + (distribution or up trap). Momentum Signals: Buy: Below Nash + positive smart money + no herd buying. Sell: Above Nash + negative smart money + no herd selling. Nash Reversion Signals: Buy: Below Nash + rising close (close > close ) + volume > MA. Sell: Above Nash + falling close + volume > MA. Final Signals: Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy. Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell. g. Position Management Position Sizing (Minimax Principle): Default: 1.0 (10% of equity). In Nash equilibrium: Reduced to 0.5 (conservative). During institutional volume: Increased to 1.5 (aggressive). Entries: Long: If long_signal is true and no existing long position (strategy.position_size <= 0). Short: If short_signal is true and no existing short position (strategy.position_size >= 0). Exits: Stop-Loss: If use_sl=true, set at 2% below/above entry price. Take-Profit: If use_tp=true, set at 5% above/below entry price. Pyramiding: Up to 3 concurrent positions allowed. h. Visualization Nash Bands: Orange (mean), red (upper), green (lower). Background Colors: Herd buying: Red (90% transparency). Herd selling: Green. Institutional volume: Blue. Signals: Contrarian buy/sell: Green/red triangles below/above bars. Liquidity traps: Red/green crosses above/below bars. Tables: Game Theory Table (Top-Right): Herd Behavior: Buying frenzy, selling panic, or normal. Institutional Flow: Accumulation, distribution, or neutral. Nash Equilibrium: In equilibrium, above, or below. Liquidity Status: Trap detected or safe. Position Suggestion: Long (green), Short (red), or Wait (gray). Backtest Table (Bottom-Right): Total Trades: Number of closed trades. Win Rate: Percentage of winning trades. Net Profit/Loss: In USD, colored green/red. Profit Factor: Gross profit / gross loss. Max Drawdown: Peak-to-trough equity drop (%). Win/Loss Trades: Number of winning/losing trades. Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown). Avg Win/Loss Ratio: Average win per trade / average loss per trade. Last Update: Current time. i. Backtesting Metrics Tracks: Total trades, winning/losing trades. Win rate (%). Net profit ($). Profit factor (gross profit / gross loss). Max drawdown (%). Simplified Sharpe ratio (returns / drawdown). Average win/loss ratio. Updates metrics on each closed trade. Displays a label on the last bar with backtest period, total trades, win rate, and net profit. j. Alerts No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")). Use TradingView's alert system with Strategy Tester outputs. 3. Usage Tips Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals. Settings: Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping). Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets. Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto). Nash Period: Increase to 200 for longer-term analysis. Backtesting: Use TradingView's Strategy Tester to evaluate performance. Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability. Test on different assets/timeframes to ensure robustness. Live Trading: Start with a demo account. Combine with other indicators (e.g., EMAs, support/resistance) for confirmation. Monitor liquidity traps and institutional flow for context. Risk Management: Always use SL/TP to limit losses. Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading). Avoid over-leveraging (pyramiding=3 can amplify risk). Troubleshooting: If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity). Ensure sufficient historical data for Nash and liquidity calculations. If tables overlap, adjust position.top_right/bottom_right coordinates. 4. Key Differences from the Previous Indicator Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting. Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones. Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns. Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator. Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy. Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator. 5. Conclusion The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. It’s designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management. If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
Estrategia Pine Script®
por EfeErdost
Live Market - Performance MonitorLive Market — Performance Monitor Study material (no code) — step-by-step training guide for learners ________________________________________ 1) What this tool is — short overview This indicator is a live market performance monitor designed for learning. It scans price, volume and volatility, detects order blocks and trendline events, applies filters (volume & ATR), generates trade signals (BUY/SELL), creates simple TP/SL trade management, and renders a compact dashboard summarizing market state, risk and performance metrics. Use it to learn how multi-factor signals are constructed, how Greeks-style sensitivity is replaced by volatility/ATR reasoning, and how a live dashboard helps monitor trade quality. ________________________________________ 2) Quick start — how a learner uses it (step-by-step) 1. Add the indicator to a chart (any ticker / timeframe). 2. Open inputs and review the main groups: Order Block, Trendline, Signal Filters, Display. 3. Start with defaults (OB periods ≈ 7, ATR multiplier 0.5, volume threshold 1.2) and observe the dashboard on the last bar. 4. Walk the chart back in time (use the last-bar update behavior) and watch how signals, order blocks, trendlines, and the performance counters change. 5. Run the hands-on labs below to build intuition. ________________________________________ 3) Main configurable inputs (what you can tweak) • Order Block Relevant Periods (default ~7): number of consecutive candles used to define an order block. • Min. Percent Move for Valid OB (threshold): minimum percent move required for a valid order block. • Number of OB Channels: how many past order block lines to keep visible. • Trendline Period (tl_period): pivot lookback for detecting highs/lows used to draw trendlines. • Use Wicks for Trendlines: whether pivot uses wicks or body. • Extension Bars: how far trendlines are projected forward. • Use Volume Filter + Volume Threshold Multiplier (e.g., 1.2): requires volume to be greater than multiplier × average volume. • Use ATR Filter + ATR Multiplier: require bar range > ATR × multiplier to filter noise. • Show Targets / Table settings / Colors for visualization. ________________________________________ 4) Core building blocks — what the script computes (plain language) Price & trend: • Spot / LTP: current close price. • EMA 9 / 21 / 50: fast, medium, slow moving averages to define short/medium trend. o trend_bullish: EMA9 > EMA21 > EMA50 o trend_bearish: EMA9 < EMA21 < EMA50 o trend_neutral: otherwise Volatility & noise: • ATR (14): average true range used for dynamic target and filter sizing. • dynamic_zone = ATR × atr_multiplier: minimum bar range required for meaningful move. • Annualized volatility: stdev of price changes × sqrt(252) × 100 — used to classify volatility (HIGH/MEDIUM/LOW). Momentum & oscillators: • RSI 14: overbought/oversold indicator (thresholds 70/30). • MACD: EMA(12)-EMA(26) and a 9-period signal line; histogram used for momentum direction and strength. • Momentum (ta.mom 10): raw momentum over 10 bars. Mean reversion / band context: • Bollinger Bands (20, 2σ): upper, mid, lower. o price_position measures where price sits inside the band range as 0–100. Volume metrics: • avg_volume = SMA(volume, 20) and volume_spike = volume > avg_volume × volume_threshold o volume_ratio = volume / avg_volume Support & Resistance: • support_level = lowest low over 20 bars • resistance_level = highest high over 20 bars • current_position = percent of price between support & resistance (0–100) ________________________________________ 5) Order Block detection — concept & logic What it tries to find: a bar (the base) followed by N candles in the opposite direction (a classical order block setup), with a minimum % move to qualify. The script records the high/low of the base candle, averages them, and plots those levels as OB channels. How learners should think about it (conceptual): 1. An order block is a signature area where institutions (theory) left liquidity — often seen as a large bar followed by a sequence of directional candles. 2. This indicator uses a configurable number of subsequent candles to confirm that the pattern exists. 3. When found, it stores and displays the base candle’s high/low area so students can see how price later reacts to those zones. Implementation note for learners: the tool keeps a limited history of OB lines (ob_channels). When new OBs exceed the count, the oldest lines are removed — good practice to avoid clutter. ________________________________________ 6) Trendline detection — idea & interpretation • The script finds pivot highs and lows using a symmetric lookback (tl_period and half that as right/left). • It then computes a trendline slope from successive pivots and projects the line forward (extension_bars). • Break detection: Resistance break = close crosses above the projected resistance line; Support break = close crosses below projected support. Learning tip: trendlines here are computed from pivot points and time. Watch how changing tl_period (bigger = smoother, fewer pivots) alters the trendlines and break signals. ________________________________________ 7) Signal generation & filters — step-by-step 1. Primary triggers: o Bullish trigger: order block bullish OR resistance trendline break. o Bearish trigger: bearish order block OR support trendline break. 2. Filters applied (both must pass unless disabled): o Volume filter: volume must be > avg_volume × volume_threshold. o ATR filter: bar range (high-low) must exceed ATR × atr_multiplier. o Not in an existing trade: new trades only start if trade_active is false. 3. Trend confirmation: o The primary trigger is only confirmed if trend is bullish/neutral for buys or bearish/neutral for sells (EMA alignment). 4. Result: o When confirmed, a long or short trade is activated with TP/SL calculated from ATR multiples. ________________________________________ 8) Trade management — what the tool does after a signal • Entry management: the script marks a trade as trade_active and sets long_trade or short_trade flags. • TP & SL rules: o Long: TP = high + 2×ATR ; SL = low − 1×ATR o Short: TP = low − 2×ATR ; SL = high + 1×ATR • Monitoring & exit: o A trade closes when price reaches TP or SL. o When TP/SL hit, the indicator updates win_count and total_pnl using a very simple calculation (difference between TP/SL and previous close). o Visual lines/labels are drawn for TP and updated as the trade runs. Important learner notes: • The script does not store a true entry price (it uses close in its P&L math), so PnL is an approximation — treat this as a learning proxy, not a position accounting system. • There’s no sizing, slippage, or fee accounted — students must manually factor these when translating to real trades. • This indicator is not a backtesting strategy; strategy.* functions would be needed for rigorous backtest results. ________________________________________ 9) Signal strength & helper utilities • Signal strength is a composite score (0–100) made up of four signals worth 25 points each: 1. RSI extreme (overbought/oversold) → 25 2. Volume spike → 25 3. MACD histogram magnitude increasing → 25 4. Trend existence (bull or bear) → 25 • Progress bars (text glyphs) are used to visually show RSI and signal strength on the table. Learning point: composite scoring is a way to combine orthogonal signals — study how changing weights changes outcomes. ________________________________________ 10) Dashboard — how to read each section (walkthrough) The dashboard is split into sections; here's how to interpret them: 1. Market Overview o LTP / Change%: immediate price & daily % change. 2. RSI & MACD o RSI value plus progress bar (overbought 70 / oversold 30). o MACD histogram sign indicates bullish/bearish momentum. 3. Volume Analysis o Volume ratio (current / average) and whether there’s a spike. 4. Order Block Status o Buy OB / Sell OB: the average base price of detected order blocks or “No Signal.” 5. Signal Status o 🔼 BUY or 🔽 SELL if confirmed, or ⚪ WAIT. o No-trade vs Active indicator summarizing market readiness. 6. Trend Analysis o Trend direction (from EMAs), market sentiment score (composite), volatility level and band/position metrics. 7. Performance o Win Rate = wins / signals (percentage) o Total PnL = cumulative PnL (approximate) o Bull / Bear Volume = accumulated volumes attributable to signals 8. Support & Resistance o 20-bar highest/lowest — use as nearby reference points. 9. Risk & R:R o Risk Level from ATR/price as a percent. o R:R Ratio computed from TP/SL if a trade is active. 10. Signal Strength & Active Trade Status • Numeric strength + progress bar and whether a trade is currently active with TP/SL display. ________________________________________ 11) Alerts — what will notify you The indicator includes pre-built alert triggers for: • Bullish confirmed signal • Bearish confirmed signal • TP hit (long/short) • SL hit (long/short) • No-trade zone • High signal strength (score > 75%) Training use: enable alerts during a replay session to be notified when the indicator would have signalled. ________________________________________ 12) Labs — hands-on exercises for learners (step-by-step) Lab A — Order Block recognition 1. Pick a 15–30 minute timeframe on a liquid ticker. 2. Use default OB periods (7). Mark each time the dashboard shows a Buy/Sell OB. 3. Manually inspect the chart at the base candle and the following sequence — draw the OB zone by hand and watch later price reactions to it. 4. Repeat with OB periods 5 and 10; note stability vs noise. Lab B — Trendline break confirmation 1. Increase trendline period (e.g., 20), watch trendlines form from pivots. 2. When a resistance break is flagged, compare with MACD & volume: was momentum aligned? 3. Note false breaks vs confirmed moves — change extension_bars to see projection effects. Lab C — Filter sensitivity 1. Toggle Use Volume Filter off, and record the number and quality of signals in a 2-day window. 2. Re-enable volume filter and change threshold from 1.2 → 1.6; note how many low-quality signals are filtered out. Lab D — Trade management simulation 1. For each signalled trade, record the time, close entry approximation, TP, SL, and eventual hit/miss. 2. Compute actual PnL if you had entered at the open of the next bar to compare with the script’s PnL math. 3. Tabulate win rate and average R:R. Lab E — Performance review & improvement 1. Build a spreadsheet of signals over 30–90 periods with columns: Date, Signal type, Entry price (real), TP, SL, Exit, PnL, Notes. 2. Analyze which filters or indicators contributed most to winners vs losers and adjust weights. ________________________________________ 13) Common pitfalls, assumptions & implementation notes (things to watch) • P&L simplification: total_pnl uses close as a proxy entry price. Real entry/exit prices and slippage are not recorded — so PnL is approximate. • No position sizing or money management: the script doesn’t compute position size from equity or risk percent. • Signal confirmation logic: composite "signal_strength" is a simple 4×25 point scheme — explore different weights or additional signals. • Order block detection nuance: the script defines the base candle and checks the subsequent sequence. Be sure to verify whether the intended candle direction (base being bullish vs bearish) aligns with academic/your trading definition — read the code carefully and test. • Trendline slope over time: slope is computed using timestamps; small differences may make lines sensitive on very short timeframes — using bar_index differences is usually more stable. • Not a true backtester: to evaluate performance statistically you must transform the logic into a strategy script that places hypothetical orders and records exact entry/exit prices. ________________________________________ 14) Suggested improvements for advanced learners • Record true entry price & timestamp for accurate PnL. • Add position sizing: risk % per trade using SL distance and account size. • Convert to strategy. (Pine Strategy)* to run formal backtests with equity curves, drawdowns, and metrics (Sharpe, Sortino). • Log trades to an external spreadsheet (via alerts + webhook) for offline analysis. • Add statistics: average win/loss, expectancy, max drawdown. • Add additional filters: news time blackout, market session filters, multi-timeframe confirmation. • Improve OB detection: combine wick/body, volume spike at base bar, and liquidity sweep detection. ________________________________________ 15) Glossary — quick definitions • ATR (Average True Range): measure of typical range; used to size targets and stops. • EMA (Exponential Moving Average): trend smoothing giving more weight to recent prices. • RSI (Relative Strength Index): momentum oscillator; >70 overbought, <30 oversold. • MACD: momentum oscillator using difference of two EMAs. • Bollinger Bands: volatility bands around SMA. • Order Block: a base candle area with subsequent confirmation candles; a zone of institutional interest (learning model). • Pivot High/Low: local turning point defined by candles on both sides. • Signal Strength: combined score from multiple indicators. • Win Rate: proportion of signals that hit TP vs total signals. • R:R (Risk:Reward): ratio of potential reward (TP distance) to risk (entry to SL). ________________________________________ 16) Limitations & assumptions (be explicit) • This is an indicator for learning — not a trading robot or broker connection. • No slippage, fees, commissions or tie-in to real orders are considered. • The logic is heuristic (rule-of-thumb), not a guarantee of performance. • Results are sensitive to timeframe, market liquidity, and parameter choices. ________________________________________ 17) Practical classroom / study plan (4 sessions) • Session 1 — Foundations: Understand EMAs, ATR, RSI, MACD, Bollinger Bands. Run the indicator and watch how these numbers change on a single day. • Session 2 — Zones & Filters: Study order blocks and trendlines. Test volume & ATR filters and note changes in false signals. • Session 3 — Simulated trading: Manually track 20 signals, compute real PnL and compare to the dashboard. • Session 4 — Improvement plan: Propose changes (e.g., better PnL accounting, alternative OB rule) and test their impact. ________________________________________ 18) Quick reference checklist for each signal 1. Was an order block or trendline break detected? (primary trigger) 2. Did volume meet threshold? (filter) 3. Did ATR filter (bar size) show a real move? (filter) 4. Was trend aligned (EMA 9/21/50)? (confirmation) 5. Signal confirmed → mark entry approximation, TP, SL. 6. Monitor dashboard (Signal Strength, Volatility, No-trade zone, R:R). 7. After exit, log real entry/exit, compute actual PnL, update spreadsheet. ________________________________________ 19) Educational caveat & final note This tool is built for training and analysis: it helps you see how common technical building blocks combine into trade ideas, but it is not a trading recommendation. Use it to develop judgment, to test hypotheses, and to design robust systems with proper backtesting and risk control before risking capital. ________________________________________ 20) Disclaimer (must include) Training & Educational Only — This material and the indicator are provided for educational purposes only. Nothing here is investment advice or a solicitation to buy or sell financial instruments. Past simulated or historical performance does not predict future results. Always perform full backtesting and risk management, and consider seeking advice from a qualified financial professional before trading with real capital. ________________________________________
Indicador Pine Script®
por aiTrendview-Charting-System
Canuck Trading Trader StrategyCanuck Trading Trader Strategy Overview The Canuck Trading Trader Strategy is a high-performance, trend-following trading system designed for NASDAQ:TSLA on a 15-minute timeframe. Optimized for precision and profitability, this strategy leverages short-term price trends to capture consistent gains while maintaining robust risk management. Ideal for traders seeking an automated, data-driven approach to trading Tesla’s volatile market, it delivers strong returns with controlled drawdowns. Key Features Trend-Based Entries: Identifies short-term trends using a 2-candle lookback period and a minimum trend strength of 0.2%, ensuring responsive trade signals. Risk Management: Includes a configurable 3.0% stop-loss to cap losses and a 2.0% take-profit to lock in gains, balancing risk and reward. High Precision: Utilizes bar magnification for accurate backtesting, reflecting realistic trade execution with 1-tick slippage and 0.1 commission. Clean Interface: No on-chart indicators, providing a distraction-free trading experience focused on performance. Flexible Sizing: Allocates 10% of equity per trade with support for up to 2 simultaneous positions (pyramiding). Performance Highlights Backtested from March 1, 2024, to June 20, 2025, on NASDAQ:TSLA (15-minute timeframe) with $1,000,000 initial capital: Net Profit: $2,279,888.08 (227.99%) Win Rate: 52.94% (3,039 winning trades out of 5,741) Profit Factor: 3.495 Max Drawdown: 2.20% Average Winning Trade: $1,050.91 (0.55%) Average Losing Trade: $338.20 (0.18%) Sharpe Ratio: 2.468 Note: Past performance is not indicative of future results. Always validate with your own backtesting and forward testing. Usage Instructions Setup: Apply the strategy to a NASDAQ:TSLA 15-minute chart. Ensure your TradingView account supports bar magnification for accurate results. Configuration: Lookback Candles: Default is 2 (recommended). Min Trend Strength: Set to 0.2% for optimal trade frequency. Stop Loss: Default 3.0% to cap losses. Take Profit: Default 2.0% to secure gains. Order Size: 10% of equity per trade. Pyramiding: Allows up to 2 orders. Commission: Set to 0.1. Slippage: Set to 1 tick. Enable "Recalculate After Order is Filled" and "Recalculate on Every Tick" in backtest settings. Backtesting: Run backtests over March 1, 2024, to June 20, 2025, to verify performance. Adjust stop-loss (e.g., 2.5%) or take-profit (e.g., 1–3%) to suit your risk tolerance. Live Trading: Use with a compatible broker or TradingView alerts for automated execution. Monitor execution for slippage or latency, especially given the high trade frequency (5,741 trades). Validate in a demo account before deploying with real capital. Risk Disclosure Trading involves significant risk and may result in losses exceeding your initial capital. The Canuck Trading Trader Strategy is provided for educational and informational purposes only. Users are responsible for their own trading decisions and should conduct thorough testing before using in live markets. The strategy’s high trade frequency requires reliable execution infrastructure to minimize slippage and latency.
Estrategia Pine Script®
por ctoincanada
4
ICT Master Suite [Trading IQ]Hello Traders! We’re excited to introduce the ICT Master Suite by TradingIQ, a new tool designed to bring together several ICT concepts and strategies in one place. The Purpose Behind the ICT Master Suite There are a few challenges traders often face when using ICT-related indicators: Many available indicators focus on one or two ICT methods, which can limit traders who apply a broader range of ICT related techniques on their charts. There aren't many indicators for ICT strategy models, and we couldn't find ICT indicators that allow for testing the strategy models and setting alerts. Many ICT related concepts exist in the public domain as indicators, not strategies! This makes it difficult to verify that the ICT concept has some utility in the market you're trading and if it's worth trading - it's difficult to know if it's working! Some users might not have enough chart space to apply numerous ICT related indicators, which can be restrictive for those wanting to use multiple ICT techniques simultaneously. The ICT Master Suite is designed to offer a comprehensive option for traders who want to apply a variety of ICT methods. By combining several ICT techniques and strategy models into one indicator, it helps users maximize their chart space while accessing multiple tools in a single slot. Additionally, the ICT Master Suite was developed as a strategy . This means users can backtest various ICT strategy models - including deep backtesting. A primary goal of this indicator is to let traders decide for themselves what markets to trade ICT concepts in and give them the capability to figure out if the strategy models are worth trading! What Makes the ICT Master Suite Different There are many ICT-related indicators available on TradingView, each offering valuable insights. What the ICT Master Suite aims to do is bring together a wider selection of these techniques into one tool. This includes both key ICT methods and strategy models, allowing traders to test and activate strategies all within one indicator. Features The ICT Master Suite offers: Multiple ICT strategy models, including the 2022 Strategy Model and Unicorn Model, which can be built, tested, and used for live trading. Calculation and display of key price areas like Breaker Blocks, Rejection Blocks, Order Blocks, Fair Value Gaps, Equal Levels, and more. The ability to set alerts based on these ICT strategies and key price areas. A comprehensive, yet practical, all-inclusive ICT indicator for traders. Customizable Timeframe - Calculate ICT concepts on off-chart timeframes Unicorn Strategy Model 2022 Strategy Model Liquidity Raid Strategy Model OTE (Optimal Trade Entry) Strategy Model Silver Bullet Strategy Model Order blocks Breaker blocks Rejection blocks FVG Strong highs and lows Displacements Liquidity sweeps Power of 3 ICT Macros HTF previous bar high and low Break of Structure indications Market Structure Shift indications Equal highs and lows Swings highs and swing lows Fibonacci TPs and SLs Swing level TPs and SLs Previous day high and low TPs and SLs And much more! An ongoing project! How To Use Many traders will already be familiar with the ICT related concepts listed above, and will find using the ICT Master Suite quite intuitive! Despite this, let's go over the features of the tool in-depth and how to use the tool! The image above shows the ICT Master Suite with almost all techniques activated. ICT 2022 Strategy Model The ICT Master suite provides the ability to test, set alerts for, and live trade the ICT 2022 Strategy Model. The image above shows an example of a long position being entered following a complete setup for the 2022 ICT model. A liquidity sweep occurs prior to an upside breakout. During the upside breakout the model looks for the FVG that is nearest 50% of the setup range. A limit order is placed at this FVG for entry. The target entry percentage for the range is customizable in the settings. For instance, you can select to enter at an FVG nearest 33% of the range, 20%, 66%, etc. The profit target for the model generally uses the highest high of the range (100%) for longs and the lowest low of the range (100%) for shorts. Stop losses are generally set at 0% of the range. The image above shows the short model in action! Whether you decide to follow the 2022 model diligently or not, you can still set alerts when the entry condition is met. ICT Unicorn Model The image above shows an example of a long position being entered following a complete setup for the ICT Unicorn model. A lower swing low followed by a higher swing high precedes the overlap of an FVG and breaker block formed during the sequence. During the upside breakout the model looks for an FVG and breaker block that formed during the sequence and overlap each other. A limit order is placed at the nearest overlap point to current price. The profit target for this example trade is set at the swing high and the stop loss at the swing low. However, both the profit target and stop loss for this model are configurable in the settings. For Longs, the selectable profit targets are: Swing High Fib -0.5 Fib -1 Fib -2 For Longs, the selectable stop losses are: Swing Low Bottom of FVG or breaker block The image above shows the short version of the Unicorn Model in action! For Shorts, the selectable profit targets are: Swing Low Fib -0.5 Fib -1 Fib -2 For Shorts, the selectable stop losses are: Swing High Top of FVG or breaker block The image above shows the profit target and stop loss options in the settings for the Unicorn Model. Optimal Trade Entry (OTE) Model The image above shows an example of a long position being entered following a complete setup for the OTE model. Price retraces either 0.62, 0.705, or 0.79 of an upside move and a trade is entered. The profit target for this example trade is set at the -0.5 fib level. This is also adjustable in the settings. For Longs, the selectable profit targets are: Swing High Fib -0.5 Fib -1 Fib -2 The image above shows the short version of the OTE Model in action! For Shorts, the selectable profit targets are: Swing Low Fib -0.5 Fib -1 Fib -2 Liquidity Raid Model The image above shows an example of a long position being entered following a complete setup for the Liquidity Raid Modell. The user must define the session in the settings (for this example it is 13:30-16:00 NY time). During the session, the indicator will calculate the session high and session low. Following a “raid” of either the session high or session low (after the session has completed) the script will look for an entry at a recently formed breaker block. If the session high is raided the script will look for short entries at a bearish breaker block. If the session low is raided the script will look for long entries at a bullish breaker block. For Longs, the profit target options are: Swing high User inputted Lib level For Longs, the stop loss options are: Swing low User inputted Lib level Breaker block bottom The image above shows the short version of the Liquidity Raid Model in action! For Shorts, the profit target options are: Swing Low User inputted Lib level For Shorts, the stop loss options are: Swing High User inputted Lib level Breaker block top Silver Bullet Model The image above shows an example of a long position being entered following a complete setup for the Silver Bullet Modell. During the session, the indicator will determine the higher timeframe bias. If the higher timeframe bias is bullish the strategy will look to enter long at an FVG that forms during the session. If the higher timeframe bias is bearish the indicator will look to enter short at an FVG that forms during the session. For Longs, the profit target options are: Nearest Swing High Above Entry Previous Day High For Longs, the stop loss options are: Nearest Swing Low Previous Day Low The image above shows the short version of the Silver Bullet Model in action! For Shorts, the profit target options are: Nearest Swing Low Below Entry Previous Day Low For Shorts, the stop loss options are: Nearest Swing High Previous Day High Order blocks The image above shows indicator identifying and labeling order blocks. The color of the order blocks, and how many should be shown, are configurable in the settings! Breaker Blocks The image above shows indicator identifying and labeling order blocks. The color of the breaker blocks, and how many should be shown, are configurable in the settings! Rejection Blocks The image above shows indicator identifying and labeling rejection blocks. The color of the rejection blocks, and how many should be shown, are configurable in the settings! Fair Value Gaps The image above shows indicator identifying and labeling fair value gaps. The color of the fair value gaps, and how many should be shown, are configurable in the settings! Additionally, you can select to only show fair values gaps that form after a liquidity sweep. Doing so reduces "noisy" FVGs and focuses on identifying FVGs that form after a significant trading event. The image above shows the feature enabled. A fair value gap that occurred after a liquidity sweep is shown. Market Structure The image above shows the ICT Master Suite calculating market structure shots and break of structures! The color of MSS and BoS, and whether they should be displayed, are configurable in the settings. Displacements The images above show indicator identifying and labeling displacements. The color of the displacements, and how many should be shown, are configurable in the settings! Equal Price Points The image above shows the indicator identifying and labeling equal highs and equal lows. The color of the equal levels, and how many should be shown, are configurable in the settings! Previous Custom TF High/Low The image above shows the ICT Master Suite calculating the high and low price for a user-defined timeframe. In this case the previous day’s high and low are calculated. To illustrate the customizable timeframe function, the image above shows the indicator calculating the previous 4 hour high and low. Liquidity Sweeps The image above shows the indicator identifying a liquidity sweep prior to an upside breakout. The image above shows the indicator identifying a liquidity sweep prior to a downside breakout. The color and aggressiveness of liquidity sweep identification are adjustable in the settings! Power Of Three The image above shows the indicator calculating Po3 for two user-defined higher timeframes! Macros The image above shows the ICT Master Suite identifying the ICT macros! ICT Macros are only displayable on the 5 minute timeframe or less. Strategy Performance Table In addition to a full-fledged TradingView backtest for any of the ICT strategy models the indicator offers, a quick-and-easy strategy table exists for the indicator! The image above shows the strategy performance table in action. Keep in mind that, because the ICT Master Suite is a strategy script, you can perform fully automatic backtests, deep backtests, easily add commission and portfolio balance and look at pertinent metrics for the ICT strategies you are testing! Lite Mode Traders who want the cleanest chart possible can toggle on “Lite Mode”! In Lite Mode, any neon or “glow” like effects are removed and key levels are marked as strict border boxes. You can also select to remove box borders if that’s what you prefer! Settings Used For Backtest For the displayed backtest, a starting balance of $1000 USD was used. A commission of 0.02%, slippage of 2 ticks, a verify price for limit orders of 2 ticks, and 5% of capital investment per order. A commission of 0.02% was used due to the backtested asset being a perpetual future contract for a crypto currency. The highest commission (lowest-tier VIP) for maker orders on many exchanges is 0.02%. All entered positions take place as maker orders and so do profit target exits. Stop orders exist as stop-market orders. A slippage of 2 ticks was used to simulate more realistic stop-market orders. A verify limit order settings of 2 ticks was also used. Even though BTCUSDT.P on Binance is liquid, we just want the backtest to be on the safe side. Additionally, the backtest traded 100+ trades over the period. The higher the sample size the better; however, this example test can serve as a starting point for traders interested in ICT concepts. Community Assistance And Feedback Given the complexity and idiosyncratic applications of ICT concepts amongst its proponents, the ICT Master Suite’s built-in strategies and level identification methods might not align with everyone's interpretation. That said, the best we can do is precisely define ICT strategy rules and concepts to a repeatable process, test, and apply them! Whether or not an ICT strategy is trading precisely how you would trade it, seeing the model in action, taking trades, and with performance statistics is immensely helpful in assessing predictive utility. If you think we missed something, you notice a bug, have an idea for strategy model improvement, please let us know! The ICT Master Suite is an ongoing project that will, ideally, be shaped by the community. A big thank you to the @PineCoders for their Time Library! Thank you!
Estrategia Pine Script®
por Trading-IQ
34
Trailing Take Profit - Close Based📝 Description This script demonstrates a new approach to the trailing take profit. Trailing Take Profit is a price-following technique. When used, instead of setting a limit order for the take profit target exiting from your position at the specified price, a stop order is conditionally set when the take profit target is reached. Then, the stop price (a.k.a trailing price), is placed below the take profit target at a distance defined by the user percentagewise. On regular time intervals, the stop price gets updated by following the "Trail Barrier" price (high by default) upwards. When the current price hits the stop price you exit the trade. Check the chart for more details. This script demonstrates how to implement the close-based Trailing Take Profit logic for long positions, but it can also be applied for short positions if the logic is "reversed". 📢 NOTE To generate some entries and showcase the "Trailing Take Profit" technique, this script uses the crossing of two moving averages. Please keep in mind that you should not relate the Backtesting results you see in the "Strategy Tester" tab with the success of the technique itself. This is not a complete strategy per se, and the backtest results are affected by many parameters that are outside of the scope of this publication. If you choose to use this new approach of the "Trailing Take Profit" in your logic you have to make sure that you are backtesting the whole strategy. ⚔️ Comparison In contrast to my older "Trailing Take Profit" publication where the trailing take profit implementation was tick-based, this new approach is close-based, meaning that the update of the stop price occurs at the bar close instead of every tick. While comparing the real-time results of the two implementations is like comparing apples to oranges, because they have different dynamic behavior, the new approach offers better consistency between the backtesting results and the real-time results. By updating the stop price on every bar close, you do not rely on the backtester assumptions anymore (check the Reasoning section below for more info). The new approach resembles the conditional "Trailing Exit" technique, where the condition is true when the current price crosses over the take profit target. Then, the stop order is placed at the trailing price and it gets updated on every bar close to "follow" the barrier price (high). On the other hand, the older tick-based approach had more "tight" dynamics since the trailing price gets updated on every tick leaving less room for price fluctuations by making it more probable to reach the trailing price. 🤔 Reasoning This new close-based approach addresses several practical issues the older tick-based approach had. Those issues arise mainly from the technicalities of the TV Backtester. More specifically, due to the assumptions the Broker Emulator makes for the price action of the history bars, the backtesting results in the TV Backtester are exaggerated, and depending on the timeframe, the backtesting results look way better than they are in reality. The effect above, and the inability to reason about the performance of a strategy separated people into two groups. Those who never use this feature, because they couldn't know for sure the actual effect it might have in their strategy, (even if it turned out to be more profitable) and those who abused this type of "repainting" behavior to show off, and hijack some boosts from the community by boasting about the "fake" results of their strategies. Even if there are ways to evaluate the effectiveness of the tick-based approach that is applied in an existing strategy (this is out of the topic of this publication), it requires extra effort to do the analysis. Using this closed-based approach we can have more predictable results, without surprises. ⚠️ Caveats Since this approach updates the trailing price on bar close, you must wait for at least one bar to close after the price crosses over the take profit target.
Estrategia Pine Script®
por jason5480
1
Alert(), alertcondition() or strategy alerts?Variety of possibilities offered by PineScript, especially thanks to recent additions, created some confusion. Especially one question repeats quite often - which method to use to trigger alerts? I'm posting this to clarify and give some syntax examples. I'll discuss these 3 methods in chronological order, meaning - in the order they were introduced to PineScript. ALERTCONDITION() - it is a function call, which can be used only in study-type script. Since years ago, you could create 2 types of a script: strategy and study. First one enables creating a backtest of a strategy. Second was to develop scripts which didn't require backtesting and could trigger alerts. alertcondition() calls in strategy-type scripts were rejected by Pine compiler. On the other hand compiling study-type scripts rejected all strategy...() calls. That created difficulties, because once you had a nice and backtested strategy, you had to rip it off from all strategy...() function calls to convert your script to study-type so you could produce alerts. Maintenance of two versions of each script was necessary and it was painful. "STRATEGY ALERTS" were introduced because of alertcondition() pains. To create strategy alert, you need to click "Add alert" button inside Strategy Tester (backtester) and only there. Alerts set-up this way are bound with the backtester - whenever backtester triggers an order, which is visible on the chart, alert is also fired. And you can customize alert message using some placeholders like {{strategy.order.contracts}} or {{ticker}}. ALERT() was added last. This is an alerts-triggering function call, which can be run from strategy-type script. Finally it is doable! You can connect it to any event coded in PineScript and generate any alert message you want, thanks to concatenation of strings and wrapping variables into tostring() function. Out of these three alertcondition() is obviously archaic and probably will be discontinued. There is a chance this makes strategy/study distinction not making sense anymore, so I wouldn't be surprised if "studies" are deprecated at some point. But what are the differences between "Strategy alerts" and alert()? "Strategy alerts" seem easier to set-up with just a few clicks and probably easier to understand and verify, because they go in sync with the backtester and on-chart trade markers. It is especially important to understand how they work if you're building strategy based on pending orders (stop and limit) - events in your code might trigger placing pending order, but alert will be triggered only (and when) such order is executed. But "Strategy Alerts" have some limitations - not every variable you'd like to include in alert message is available from PineScript. And maybe you don't need the alert fired when the trade hit a stop-loss or take-profit, because you have already forwarded info about closing conditions in entry alert to your broker/exchange. Alert() was added to PineScript to fill all these gaps. Is allows concatenating any alert message you want, with any variable you want inside it and you can attach alert() function at any event in your PineScript code. For example - when placing orders, crossing variables, exiting trades, but not explicitly at pending orders execution. The Verdict "Strategy Alerts" might seem a better fit - easier to set-up and verify, flexible and they fire only when a trade really happens, not producing unnecessary mess when each pending order is placed. But these advantages are illusionary, because they don't give you the full-control which is needed when trading with real money. Especially when using pending orders. If an alert is fired when price actually hit a stop-order or limit-order level, and even if you are executing such alert within 1 second thanks to a tool like TradingConnector, you might already be late and you are making entry at a market price. Slippage will play a great role here. You need to send ordering alert when logical conditions are met - then it will be executed at the price you want. Even if you need to cancel all the pending orders which were not executed. Because of that I strongly recommend sticking to ALERT() when building your alerts system. Below is an example strategy, showing syntax to manage placing the orders and cancelling them. Yes, this is another spin-off from my TradingView Alerts to MT4 MT5 . As usual, please don't pay attention to backtest results, as this is educational script only. P.S. For the last time - farewell alertcondition(). You served us well.
Estrategia Pine Script®
por Peter_O
16
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain? This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions. How does a Markov Chain work, and how should I read this spheres-and-arrows diagram? Think of three weather modes: Sunny, Rainy, Cloudy. Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow). The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy). Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step. Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny. Now relabel the spheres to markets: Bull, Bear, Neutral. Spheres: market regimes (uptrend, downtrend, range). Self‑loop: tendency for the current regime to continue on the next bar. Arrows: the most common next regime if a switch happens. How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear). What states and transitions are shown? The three market states visualized are: Bullish (Bull): Upward or strong-market regime. Bearish (Bear): Downward or weak-market regime. Neutral: Sideways or range-bound regime. Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull). How does the regime detection system work? You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.). Values are statistically normalized (Z-scored) over a configurable lookback period. The normalized outputs are classified into Bull, Bear, or Neutral zones. If using three indicators, their regime signals are averaged and smoothed for robustness. How are transition probabilities calculated? On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions. The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear). Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time. What is unique about the visualization? 3D animated spheres represent each regime and change visually when active. Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows. Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed. All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format. Can I use custom indicators for regime classification? Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement. What does the “Lookback Period” control? Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt. How is this different from a Hidden Markov Model (HMM)? It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust. How is this Markov Chain different from a Hidden Markov Model (HMM)? Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable. Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training. A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data. Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune. HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data. How does the indicator avoid “repainting” or look-ahead bias? All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation. Are there practical tuning tips? Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability. Use custom indicators if your asset has unique regime drivers. Watch for rapid changes in transition probabilities as early warning of a possible regime shift. Who is this indicator for? Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models. Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting: The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear). Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions. Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies. It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes. The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting. Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis. What's the mathematical foundation behind this? The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM). How to use this indicator programmatically? The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading. Step-by-Step Integration with Pine Script (input.source) Add the Markov Chain indicator to your chart. This must be done first, since your custom script will "pull" the bias signal from the indicator's plot. In your strategy, create an input using input.source() Example: //@version=5 strategy("MC Bias Strategy Example") mcBias = input.source(close, "MC Bias Source") After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot). Use the bias in your trading logic Example (long only on Bull, flat otherwise): if mcBias == 1 strategy.entry("Long", strategy.long) else strategy.close("Long") For more advanced workflows, combine mcBias with additional filters or trailing stops. How does this work behind-the-scenes? TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source). The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.). This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code. Integrating 45+ Indicators with Your Markov Chain — How & Why The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots. // Enhanced Custom Indicators Export - 45 Technical Indicators // Comprehensive technical analysis suite for advanced market regime detection //@version=6 indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500) // |----- Input Parameters -----| // momentum_group = "Momentum Indicators" trend_group = "Trend Indicators" volume_group = "Volume Indicators" volatility_group = "Volatility Indicators" oscillator_group = "Oscillator Indicators" display_group = "Display Settings" // Common lengths length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group) length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group) length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group) // Display options show_table = input.bool(true, "Show Values Table", group=display_group) table_size = input.string("Small", "Table Size", options= , group=display_group) // |----- MOMENTUM INDICATORS (15 indicators) -----| // // 1. RSI (Relative Strength Index) rsi_14 = ta.rsi(close, length_14) rsi_centered = rsi_14 - 50 // 2. Stochastic Oscillator stoch_k = ta.stoch(close, high, low, length_14) stoch_d = ta.sma(stoch_k, 3) stoch_centered = stoch_k - 50 // 3. Williams %R williams_r = ta.stoch(close, high, low, length_14) - 100 // 4. MACD (Moving Average Convergence Divergence) = ta.macd(close, 12, 26, 9) // 5. Momentum (Rate of Change) momentum = ta.mom(close, length_14) momentum_pct = (momentum / close ) * 100 // 6. Rate of Change (ROC) roc = ta.roc(close, length_14) // 7. Commodity Channel Index (CCI) cci = ta.cci(close, length_20) // 8. Money Flow Index (MFI) mfi = ta.mfi(close, length_14) mfi_centered = mfi - 50 // 9. Awesome Oscillator (AO) ao = ta.sma(hl2, 5) - ta.sma(hl2, 34) // 10. Accelerator Oscillator (AC) ac = ao - ta.sma(ao, 5) // 11. Chande Momentum Oscillator (CMO) cmo = ta.cmo(close, length_14) // 12. Detrended Price Oscillator (DPO) dpo = close - ta.sma(close, length_20) // 13. Price Oscillator (PPO) ppo = ta.sma(close, 12) - ta.sma(close, 26) ppo_pct = (ppo / ta.sma(close, 26)) * 100 // 14. TRIX trix_ema1 = ta.ema(close, length_14) trix_ema2 = ta.ema(trix_ema1, length_14) trix_ema3 = ta.ema(trix_ema2, length_14) trix = ta.roc(trix_ema3, 1) * 10000 // 15. Klinger Oscillator klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55) // 16. Fisher Transform fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25 fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2)) // 17. Stochastic RSI stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14) stoch_rsi_centered = stoch_rsi - 50 // 18. Relative Vigor Index (RVI) rvi_num = ta.swma(close - open) rvi_den = ta.swma(high - low) rvi = rvi_den != 0 ? rvi_num / rvi_den : 0 // 19. Balance of Power (BOP) bop = (close - open) / (high - low) // |----- TREND INDICATORS (10 indicators) -----| // // 20. Simple Moving Average Momentum sma_20 = ta.sma(close, length_20) sma_momentum = ((close - sma_20) / sma_20) * 100 // 21. Exponential Moving Average Momentum ema_20 = ta.ema(close, length_20) ema_momentum = ((close - ema_20) / ema_20) * 100 // 22. Parabolic SAR sar = ta.sar(0.02, 0.02, 0.2) sar_trend = close > sar ? 1 : -1 // 23. Linear Regression Slope lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1) // 24. Moving Average Convergence (MAC) mac = ta.sma(close, 10) - ta.sma(close, 30) // 25. Trend Intensity Index (TII) tii_sum = 0.0 for i = 1 to length_20 tii_sum += close > close ? 1 : 0 tii = (tii_sum / length_20) * 100 // 26. Ichimoku Cloud Components ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2 ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2 ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1 // 27. MESA Adaptive Moving Average (MAMA) mama_alpha = 2.0 / (length_20 + 1) mama = ta.ema(close, length_20) mama_momentum = ((close - mama) / mama) * 100 // 28. Zero Lag Exponential Moving Average (ZLEMA) zlema_lag = math.round((length_20 - 1) / 2) zlema_data = close + (close - close ) zlema = ta.ema(zlema_data, length_20) zlema_momentum = ((close - zlema) / zlema) * 100 // |----- VOLUME INDICATORS (6 indicators) -----| // // 29. On-Balance Volume (OBV) obv = ta.obv // 30. Volume Rate of Change (VROC) vroc = ta.roc(volume, length_14) // 31. Price Volume Trend (PVT) pvt = ta.pvt // 32. Negative Volume Index (NVI) nvi = 0.0 nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi // 33. Positive Volume Index (PVI) pvi = 0.0 pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi // 34. Volume Oscillator vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10) // 35. Ease of Movement (EOM) eom_distance = high - low eom_box_height = volume / 1000000 eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0 eom_sma = ta.sma(eom, length_14) // 36. Force Index force_index = volume * (close - close ) force_index_sma = ta.sma(force_index, length_14) // |----- VOLATILITY INDICATORS (10 indicators) -----| // // 37. Average True Range (ATR) atr = ta.atr(length_14) atr_pct = (atr / close) * 100 // 38. Bollinger Bands Position bb_basis = ta.sma(close, length_20) bb_dev = 2.0 * ta.stdev(close, length_20) bb_upper = bb_basis + bb_dev bb_lower = bb_basis - bb_dev bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0 bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0 // 39. Keltner Channels Position kc_basis = ta.ema(close, length_20) kc_range = ta.ema(ta.tr, length_20) kc_upper = kc_basis + (2.0 * kc_range) kc_lower = kc_basis - (2.0 * kc_range) kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0 // 40. Donchian Channels Position dc_upper = ta.highest(high, length_20) dc_lower = ta.lowest(low, length_20) dc_basis = (dc_upper + dc_lower) / 2 dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0 // 41. Standard Deviation std_dev = ta.stdev(close, length_20) std_dev_pct = (std_dev / close) * 100 // 42. Relative Volatility Index (RVI) rvi_up = ta.stdev(close > close ? close : 0, length_14) rvi_down = ta.stdev(close < close ? close : 0, length_14) rvi_total = rvi_up + rvi_down rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50 // 43. Historical Volatility hv_returns = math.log(close / close ) hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100 // 44. Garman-Klass Volatility gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open) gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100 // 45. Parkinson Volatility park_vol = math.log(high/low) * math.log(high/low) parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100 // 46. Rogers-Satchell Volatility rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open) rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100 // |----- OSCILLATOR INDICATORS (5 indicators) -----| // // 47. Elder Ray Index elder_bull = high - ta.ema(close, 13) elder_bear = low - ta.ema(close, 13) elder_power = elder_bull + elder_bear // 48. Schaff Trend Cycle (STC) stc_macd = ta.ema(close, 23) - ta.ema(close, 50) stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10) stc_d = ta.ema(stc_k, 3) stc = ta.stoch(stc_d, stc_d, stc_d, 10) // 49. Coppock Curve coppock_roc1 = ta.roc(close, 14) coppock_roc2 = ta.roc(close, 11) coppock = ta.wma(coppock_roc1 + coppock_roc2, 10) // 50. Know Sure Thing (KST) kst_roc1 = ta.roc(close, 10) kst_roc2 = ta.roc(close, 15) kst_roc3 = ta.roc(close, 20) kst_roc4 = ta.roc(close, 30) kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15) // 51. Percentage Price Oscillator (PPO) ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100 ppo_signal = ta.ema(ppo_line, 9) ppo_histogram = ppo_line - ppo_signal // |----- PLOT MAIN INDICATORS -----| // // Plot key momentum indicators plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1) plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1) plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1) plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1) plot(cci, title="05_CCI", color=color.green, linewidth=1) // Plot trend indicators plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1) plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1) plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1) plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1) plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1) // Plot volatility indicators plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1) plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1) plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1) plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1) plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1) // Plot volume indicators plot(vroc, title="16_VROC", color=color.blue, linewidth=1) plot(eom_sma, title="17_EOM", color=color.red, linewidth=1) plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1) plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1) plot(obv, title="20_OBV", color=color.purple, linewidth=1) // Plot additional oscillators plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1) plot(cmo, title="22_CMO", color=color.maroon, linewidth=1) plot(dpo, title="23_DPO", color=color.teal, linewidth=1) plot(trix, title="24_TRIX", color=color.lime, linewidth=1) plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1) // Plot more momentum indicators plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1) plot(ac, title="27_AC", color=color.aqua, linewidth=1) plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1) plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1) plot(klinger, title="30_Klinger", color=color.gray, linewidth=1) // Plot trend continuation plot(tii, title="31_TII", color=color.blue, linewidth=1) plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1) plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1) plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1) plot(bop, title="35_BOP", color=color.purple, linewidth=1) // Plot volume continuation plot(nvi, title="36_NVI", color=color.navy, linewidth=1) plot(pvi, title="37_PVI", color=color.maroon, linewidth=1) plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1) plot(roc, title="39_ROC", color=color.lime, linewidth=1) plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1) // Plot volatility continuation plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1) plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1) plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1) plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1) plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1) // Plot final oscillators plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1) plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1) plot(stc, title="48_STC", color=color.green, linewidth=1) plot(coppock, title="49_Coppock", color=color.orange, linewidth=1) plot(kst, title="50_KST", color=color.purple, linewidth=1) // Plot final indicators plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1) plot(pvt, title="52_PVT", color=color.maroon, linewidth=1) // |----- Reference Lines -----| // hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1) hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1) // |----- Enhanced Information Table -----| // if show_table and barstate.islast table_position = position.top_right table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray) // Headers table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) // Key Momentum Indicators table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90)) table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size) table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size) table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size) table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size) table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size) table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size) table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size) table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size) table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size) // Key Trend Indicators table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90)) table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size) table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size) table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size) table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size) table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size) table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size) table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size) table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size) // Key Volatility Indicators table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90)) table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size) table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size) table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size) table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size) table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size) // Key Volume Indicators table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90)) table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size) // Key Oscillators table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90)) table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size) // Summary Statistics table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70)) table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size) regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL" table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size) This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator. How to use this script with the Markov Chain for research and backtesting: Add the Enhanced Indicator Export to your chart. Every calculated indicator is available as an individual data stream. Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option. In the Markov Chain indicator’s settings, turn ON the custom indicator mode. For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name. This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering. Backtest regime logic directly. Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns. The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration. Export the regime bias for programmatic use. As described above, use input.source() in your Pine Script strategy or system and link the bias output. You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes. With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you. Optimize your signal generation by backtesting across a universe of meaningful indicator blends. Enhance risk management with objective, real-time regime boundaries. Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes. Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic. Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem. References & Further Reading Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182. Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384. Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg. Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544. Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance. Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764. Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC. On Quantitative Finance and Markov Models: Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press. Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press. TradingView Pine Script Documentation: www.tradingview.com TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org What makes this indicator original and unique? - On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm. - Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script. - Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time. - Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window. - Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior. Integrate your own signals (using input.source()) - In settings, choose the Source . This is powered by input.source() . - Feed it price, an indicator you compute inside the script, or a custom composite series. - The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time. Credits: Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community. Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization. Disclaimer This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Indicador Pine Script®
por Fractalyst
6
Rifle UnifiedThis script is designed for use on 30-second charts of Dow Jones-related symbols (YM, MYM, US30). It provides automated buy and sell signals using a combination of price action, RSI (Relative Strength Index), and volume analysis. The script is intended for both live trading signals and backtesting, with configurable risk management and debugging features. Core Functionality 1. Signal Generation Logic Trigger: The algorithm looks for a sharp price move (drop or rise) of a user-defined threshold (default: 80 points) within a specified lookback window (default: 20 minutes). Levels: It monitors for price drops below specific numerical levels ending in 23, 43, or 73 (e.g., 42223, 42273). RSI Condition: When price falls below one of these levels and the RSI is below 30, the setup is considered active. Buy Signal: A buy is triggered if, after setup: Price rises back above the level, The RSI rate of change (ROC) indicates exhaustion of the drop, The current bar shows positive momentum. 2. Trade Management Stop Loss & Take Profit: Configurable fixed or trailing stop loss and take profit levels are plotted and managed automatically. Exit Signals: The script signals exit based on price action relative to these risk management levels. 3. Filters & Enhancements Parabolic Move Filter: Prevents entries during extreme price moves. Dead Cat Bounce Filter: Avoids false signals after sharp reversals. Volume Filter: Optionally requires volume conditions for trade entries (especially for shorts). Multiple Confirmation Layers : Includes checks for 5-minute RSI, momentum, and price retracement. User Inputs & Customization Trade Direction: Toggle between LONG and SHORT signal generation. Trigger Settings: Adjust thresholds for price moves, lookback windows, RSI ROC, and volume requirements. Trade Settings: Set take profit, stop loss, and trailing stop behavior. Debug & Visualization: Enable or disable various plots, labels, and debug tables for in-depth analysis. Backtesting: Integrated backtester with summary and detailed statistics tables. Technical Features Uses External Libraries: Relies on RifleShooterLib for core logic and BackTestLib for backtesting and statistics. Multi-timeframe Analysis: Incorporates both 30-second and 5-minute RSI calculations. Chart Annotations: Plots entry/exit points, risk levels, and debug information directly on the chart. Alert Conditions: Built-in alert triggers for key events (initial move, stall, entry). Intended Use Markets: Dow Jones symbols (YM, MYM, US30, or US30 CFD). Timeframe: 30-second chart. Purpose: Automated signal generation for discretionary or algorithmic trading, with robust risk management and backtesting support. Notable Customization & Extension Points Momentum Calculation: Plans to replace the current momentum measure with "sqz momentum". Displacement Logic: Future update to use "FVG concept" for displacement. High-Contrast RSI: Optional visual enhancements for RSI extremes. Time-based Stop: Consideration for adding a time-based stop mechanism. This script is highly modular, with extensive user controls, and is suitable for both live trading and historical analysis of Dow Jones index movements
Indicador Pine Script®
por BlueMagicTrading
MACD Enhanced Strategy MTF with Stop Loss [LTB]Test strategy for MACD This strategy, named "MACD Enhanced Strategy MTF with Stop Loss ," is a modified Moving Average Convergence Divergence (MACD) strategy with enhancements such as multi-timeframe (MTF) analysis, custom scoring, and a dynamic stop loss mechanism. Let’s break down how to effectively use it: Key Elements of the Strategy MACD Indicator with Modifications: The strategy uses MACD, a well-known momentum indicator, with customizable parameters: fastLength, slowLength, and signalLength represent the standard MACD settings. Instead of relying solely on MACD crossovers, it introduces scoring parameters for histogram direction (histside), indicator direction (indiside), and signal cross (crossscore). This allows for a more nuanced decision-making process when determining buy and sell signals. Multi-Timeframe Analysis (MTF): The strategy compares the current timeframe's MACD score with that of a higher timeframe (HTF). It dynamically selects the higher timeframe based on the current timeframe. For example, if the current chart period is 1, it will select 5 as the higher timeframe. This MTF approach aims to align trades with broader trends, filtering out false signals that could be present when analyzing only a single timeframe. Scoring System: A custom scoring system (count() function) is used to evaluate buy and sell signals. This includes calculations based on the direction and momentum of MACD (indi) and the histogram. The score is used to determine the strength of signals. Positive scores indicate bullish sentiment, while negative scores indicate bearish sentiment. This scoring mechanism aims to reduce the influence of noise and provide more reliable entries. Entry Conditions: Long Condition: When the Result value (a combination of MTF and current MACD analysis) changes and becomes positive, a long entry is triggered. Short Condition: When the Result changes and becomes negative, a short entry is initiated. Stop Loss Mechanism: The countstop() function calculates dynamic stop loss values for both long and short trades. It is based on the Average True Range (ATR) multiplied by a factor (Mult), providing adaptive stop loss levels depending on market volatility. The stop loss is plotted on the chart to show potential risk levels for open trades, with the line appearing only if shotsl is enabled. How to Use the Strategy To properly use the strategy, follow these steps: Parameter Optimization: Adjust the input parameters such as fastLength, slowLength, and signalLength to tune the MACD indicator to the specific asset you’re trading. The values provided are typical defaults, but optimizing these values based on backtesting can help improve performance. Customize the scoring parameters (crossscore, indiside, histside) to balance how much weight you want to put on the direction, histogram, and cross events of the MACD indicator. Select Appropriate Timeframes: This strategy employs a multi-timeframe (MTF) approach, so it's important to understand how the higher timeframe (HTF) is selected based on the current timeframe. For instance, if you are trading on a 5-minute chart, the higher timeframe will be 15 minutes, which helps filter out lower timeframe noise. Ensure you understand the relationship between the timeframe you’re using and the HTF it automatically selects. The strategy’s effectiveness can vary depending on how these timeframes align with the asset’s overall volatility. Run Backtests: Always backtest the strategy over historical data to determine its reliability for the asset and timeframes you’re interested in. Note that the MTF approach may require substantial data to capture how different timeframes interact. Use the backtest results to adjust the scoring parameters or the Stop Loss Factor (Mult) for better risk management. Stop Loss Usage: The stop loss is calculated dynamically using ATR, which means that it adjusts with changing volatility. This can be useful to avoid being stopped out too often during periods of increased volatility. The shotsl parameter can be set to true to visualize the stop loss line on the chart. This helps to monitor the protection level and make better decisions regarding holding or closing a trade manually. Entry Signals and Trade Execution: Look for changes in the Result value to determine entry points. For a long position, the Result needs to become positive, and for a short position, it must be negative. Note that the strategy's entries are more conservative because it waits for the Result to confirm the direction using multiple factors, which helps filter out false breakouts. Risk Management: The adaptive stop loss mechanism reduces the risk by basing the stop level on market volatility. However, you must still consider additional risk management practices such as position sizing and profit targets. Given the scoring mechanism, it might not enter trades frequently, which means using this strategy may result in fewer but potentially more accurate trades. It’s important to be patient and not force trades that don’t align with the calculated results. Real-Time Monitoring: Make sure to monitor trades actively. Since the strategy recalculates the score on each bar, real-time changes in the Result value could provide exit opportunities even if the stop loss isn't triggered. Summary The "MACD Enhanced Strategy MTF with Stop Loss " is a sophisticated version of the MACD strategy, enhanced with multi-timeframe analysis and adaptive stop loss. Properly using it involves optimizing MACD and scoring parameters, selecting suitable timeframes, and actively managing entries and exits based on a combination of scoring and volatility-based stop losses. Always conduct thorough backtesting before applying it in a live environment to ensure the strategy performs well on the asset you're trading.
Estrategia Pine Script®
por skigoogs
4
Dskyz (DAFE) Adaptive Regime - Quant Machine ProDskyz (DAFE) Adaptive Regime - Quant Machine Pro: Buckle up for the Dskyz (DAFE) Adaptive Regime - Quant Machine Pro, is a strategy that’s your ultimate edge for conquering futures markets like ES, MES, NQ, and MNQ. This isn’t just another script—it’s a quant-grade powerhouse, crafted with precision to adapt to market regimes, deliver multi-factor signals, and protect your capital with futures-tuned risk management. With its shimmering DAFE visuals, dual dashboards, and glowing watermark, it turns your charts into a cyberpunk command center, making trading as thrilling as it is profitable. Unlike generic scripts clogging up the space, the Adaptive Regime is a DAFE original, built from the ground up to tackle the chaos of futures trading. It identifies market regimes (Trending, Range, Volatile, Quiet) using ADX, Bollinger Bands, and HTF indicators, then fires trades based on a weighted scoring system that blends candlestick patterns, RSI, MACD, and more. Add in dynamic stops, trailing exits, and a 5% drawdown circuit breaker, and you’ve got a system that’s as safe as it is aggressive. Whether you’re a newbie or a prop desk pro, this strat’s your ticket to outsmarting the markets. Let’s break down every detail and see why it’s a must-have. Why Traders Need This Strategy Futures markets are a gauntlet—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional traps that punish the unprepared. Meanwhile, platforms are flooded with low-effort scripts that recycle old ideas with zero innovation. The Adaptive Regime stands tall, offering: Adaptive Intelligence: Detects market regimes (Trending, Range, Volatile, Quiet) to optimize signals, unlike one-size-fits-all scripts. Multi-Factor Precision: Combines candlestick patterns, MA trends, RSI, MACD, volume, and HTF confirmation for high-probability trades. Futures-Optimized Risk: Calculates position sizes based on $ risk (default: $300), with ATR or fixed stops/TPs tailored for ES/MES. Bulletproof Safety: 5% daily drawdown circuit breaker and trailing stops keep your account intact, even in chaos. DAFE Visual Mastery: Pulsing Bollinger Band fills, dynamic SL/TP lines, and dual dashboards (metrics + position) make signals crystal-clear and charts a work of art. Original Craftsmanship: A DAFE creation, built with community passion, not a rehashed clone of generic code. Traders need this because it’s a complete, adaptive system that blends quant smarts, user-friendly design, and DAFE flair. It’s your edge to trade with confidence, cut through market noise, and leave the copycats in the dust. Strategy Components 1. Market Regime Detection The strategy’s brain is its ability to classify market conditions into five regimes, ensuring signals match the environment. How It Works: Trending (Regime 1): ADX > 20, fast/slow EMA spread > 0.3x ATR, HTF RSI > 50 or MACD bullish (htf_trend_bull/bear). Range (Regime 2): ADX < 25, price range < 3% of close, no HTF trend. Volatile (Regime 3): BB width > 1.5x avg, ATR > 1.2x avg, HTF RSI overbought/oversold. Quiet (Regime 4): BB width < 0.8x avg, ATR < 0.9x avg. Other (Regime 5): Default for unclear conditions. Indicators: ADX (14), BB width (20), ATR (14, 50-bar SMA), HTF RSI (14, daily default), HTF MACD (12,26,9). Why It’s Brilliant: Regime detection adapts signals to market context, boosting win rates in trending or volatile conditions. HTF RSI/MACD add a big-picture filter, rare in basic scripts. Visualized via gradient background (green for Trending, orange for Range, red for Volatile, gray for Quiet, navy for Other). 2. Multi-Factor Signal Scoring Entries are driven by a weighted scoring system that combines candlestick patterns, trend, momentum, and volume for robust signals. Candlestick Patterns: Bullish: Engulfing (0.5), hammer (0.4 in Range, 0.2 else), morning star (0.2), piercing (0.2), double bottom (0.3 in Volatile, 0.15 else). Must be near support (low ≤ 1.01x 20-bar low) with volume spike (>1.5x 20-bar avg). Bearish: Engulfing (0.5), shooting star (0.4 in Range, 0.2 else), evening star (0.2), dark cloud (0.2), double top (0.3 in Volatile, 0.15 else). Must be near resistance (high ≥ 0.99x 20-bar high) with volume spike. Logic: Patterns are weighted higher in specific regimes (e.g., hammer in Range, double bottom in Volatile). Additional Factors: Trend: Fast EMA (20) > slow EMA (50) + 0.5x ATR (trend_bull, +0.2); opposite for trend_bear. RSI: RSI (14) < 30 (rsi_bull, +0.15); > 70 (rsi_bear, +0.15). MACD: MACD line > signal (12,26,9, macd_bull, +0.15); opposite for macd_bear. Volume: ATR > 1.2x 50-bar avg (vol_expansion, +0.1). HTF Confirmation: HTF RSI < 70 and MACD bullish (htf_bull_confirm, +0.2); RSI > 30 and MACD bearish (htf_bear_confirm, +0.2). Scoring: bull_score = sum of bullish factors; bear_score = sum of bearish. Entry requires score ≥ 1.0. Example: Bullish engulfing (0.5) + trend_bull (0.2) + rsi_bull (0.15) + htf_bull_confirm (0.2) = 1.05, triggers long. Why It’s Brilliant: Multi-factor scoring ensures signals are confirmed by multiple market dynamics, reducing false positives. Regime-specific weights make patterns more relevant (e.g., hammers shine in Range markets). HTF confirmation aligns with the big picture, a quant edge over simplistic scripts. 3. Futures-Tuned Risk Management The risk system is built for futures, calculating position sizes based on $ risk and offering flexible stops/TPs. Position Sizing: Logic: Risk per trade (default: $300) ÷ (stop distance in points * point value) = contracts, capped at max_contracts (default: 5). Point value = tick value (e.g., $12.5 for ES) * ticks per point (4) * contract multiplier (1 for ES, 0.1 for MES). Example: $300 risk, 8-point stop, ES ($50/point) → 0.75 contracts, rounded to 1. Impact: Precise sizing prevents over-leverage, critical for micro contracts like MES. Stops and Take-Profits: Fixed: Default stop = 8 points, TP = 16 points (2:1 reward/risk). ATR-Based: Stop = 1.5x ATR (default), TP = 3x ATR, enabled via use_atr_for_stops. Logic: Stops set at swing low/high ± stop distance; TPs at 2x stop distance from entry. Impact: ATR stops adapt to volatility, while fixed stops suit stable markets. Trailing Stops: Logic: Activates at 50% of TP distance. Trails at close ± 1.5x ATR (atr_multiplier). Longs: max(trail_stop_long, close - ATR * 1.5); shorts: min(trail_stop_short, close + ATR * 1.5). Impact: Locks in profits during trends, a game-changer in volatile sessions. Circuit Breaker: Logic: Pauses trading if daily drawdown > 5% (daily_drawdown = (max_equity - equity) / max_equity). Impact: Protects capital during black swan events (e.g., April 27, 2025 ES slippage). Why It’s Brilliant: Futures-specific inputs (tick value, multiplier) make it plug-and-play for ES/MES. Trailing stops and circuit breaker add pro-level safety, rare in off-the-shelf scripts. Flexible stops (ATR or fixed) suit different trading styles. 4. Trade Entry and Exit Logic Entries and exits are precise, driven by bull_score/bear_score and protected by drawdown checks. Entry Conditions: Long: bull_score ≥ 1.0, no position (position_size <= 0), drawdown < 5% (not pause_trading). Calculates contracts, sets stop at swing low - stop points, TP at 2x stop distance. Short: bear_score ≥ 1.0, position_size >= 0, drawdown < 5%. Stop at swing high + stop points, TP at 2x stop distance. Logic: Tracks entry_regime for PNL arrays. Closes opposite positions before entering. Exit Conditions: Stop-Loss/Take-Profit: Hits stop or TP (strategy.exit). Trailing Stop: Activates at 50% TP, trails by ATR * 1.5. Emergency Exit: Closes if price breaches stop (close < long_stop_price or close > short_stop_price). Reset: Clears stop/TP prices when flat (position_size = 0). Why It’s Brilliant: Score-based entries ensure multi-factor confirmation, filtering out weak signals. Trailing stops maximize profits in trends, unlike static exits in basic scripts. Emergency exits add an extra safety layer, critical for futures volatility. 5. DAFE Visuals The visuals are pure DAFE magic, blending function with cyberpunk flair to make signals intuitive and charts stunning. Shimmering Bollinger Band Fill: Display: BB basis (20, white), upper/lower (green/red, 45% transparent). Fill pulses (30–50 alpha) by regime, with glow (60–95 alpha) near bands (close ≥ 0.995x upper or ≤ 1.005x lower). Purpose: Highlights volatility and key levels with a futuristic glow. Visuals make complex regimes and signals instantly clear, even for newbies. Pulsing effects and regime-specific colors add a DAFE signature, setting it apart from generic scripts. BB glow emphasizes tradeable levels, enhancing decision-making. Chart Background (Regime Heatmap): Green — Trending Market: Strong, sustained price movement in one direction. The market is in a trend phase—momentum follows through. Orange — Range-Bound: Market is consolidating or moving sideways, with no clear up/down trend. Great for mean reversion setups. Red — Volatile Regime: High volatility, heightened risk, and larger/faster price swings—trade with caution. Gray — Quiet/Low Volatility: Market is calm and inactive, with small moves—often poor conditions for most strategies. Navy — Other/Neutral: Regime is uncertain or mixed; signals may be less reliable. Bollinger Bands Glow (Dynamic Fill): Neon Red Glow — Warning!: Price is near or breaking above the upper band; momentum is overstretched, watch for overbought conditions or reversals. Bright Green Glow — Opportunity!: Price is near or breaking below the lower band; market could be oversold, prime for bounce or reversal. Trend Green Fill — Trending Regime: Fills between bands with green when the market is trending, showing clear momentum. Gold/Yellow Fill — Range Regime: Fills with gold/aqua in range conditions, showing the market is sideways/oscillating. Magenta/Red Fill — Volatility Spike: Fills with vivid magenta/red during highly volatile regimes. Blue Fill — Neutral/Quiet: A soft blue glow for other or uncertain market states. Moving Averages: Display: Blue fast EMA (20), red slow EMA (50), 2px. Purpose: Shows trend direction, with trend_dir requiring ATR-scaled spread. Dynamic SL/TP Lines: Display: Pulsing colors (red SL, green TP for Trending; yellow/orange for Range, etc.), 3px, with pulse_alpha for shimmer. Purpose: Tracks stops/TPs in real-time, color-coded by regime. 6. Dual Dashboards Two dashboards deliver real-time insights, making the strat a quant command center. Bottom-Left Metrics Dashboard (2x13): Metrics: Mode (Active/Paused), trend (Bullish/Bearish/Neutral), ATR, ATR avg, volume spike (YES/NO), RSI (value + Oversold/Overbought/Neutral), HTF RSI, HTF trend, last signal (Buy/Sell/None), regime, bull score. Display: Black (29% transparent), purple title, color-coded (green for bullish, red for bearish). Purpose: Consolidates market context and signal strength. Top-Right Position Dashboard (2x7): Metrics: Regime, position side (Long/Short/None), position PNL ($), SL, TP, daily PNL ($). Display: Black (29% transparent), purple title, color-coded (lime for Long, red for Short). Purpose: Tracks live trades and profitability. Why It’s Brilliant: Dual dashboards cover market context and trade status, a rare feature. Color-coding and concise metrics guide beginners (e.g., green “Buy” = go). Real-time PNL and SL/TP visibility empower disciplined trading. 7. Performance Tracking Logic: Arrays (regime_pnl_long/short, regime_win/loss_long/short) track PNL and win/loss by regime (1–5). Updated on trade close (barstate.isconfirmed). Purpose: Prepares for future adaptive thresholds (e.g., adjust bull_score min based on regime performance). Why It’s Brilliant: Lays the groundwork for self-optimizing logic, a quant edge over static scripts. Key Features Regime-Adaptive: Optimizes signals for Trending, Range, Volatile, Quiet markets. Futures-Optimized: Precise sizing for ES/MES with tick-based risk inputs. Multi-Factor Signals: Candlestick patterns, RSI, MACD, and HTF confirmation for robust entries. Dynamic Exits: ATR/fixed stops, 2:1 TPs, and trailing stops maximize profits. Safe and Smart: 5% drawdown breaker and emergency exits protect capital. DAFE Visuals: Shimmering BB fill, pulsing SL/TP, and dual dashboards. Backtest-Ready: Fixed qty and tick calc for accurate historical testing. How to Use Add to Chart: Load on a 5min ES/MES chart in TradingView. Configure Inputs: Set instrument (ES/MES), tick value ($12.5/$1.25), multiplier (1/0.1), risk ($300 default). Enable ATR stops for volatility. Monitor Dashboards: Bottom-left for regime/signals, top-right for position/PNL. Backtest: Run in strategy tester to compare regimes. Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues). Replay Test: Try April 28, 2025 NQ drop to see regime shifts and stops. Disclaimer Trading futures involves significant risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Backtest results may differ from live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred. Backtesting: Frame: 2023-09-20 - 2025-04-29 Slippage: 3 Fee Typical Range (per side, per contract) CME Exchange $1.14 – $1.20 Clearing $0.10 – $0.30 NFA Regulatory $0.02 Firm/Broker Commis. $0.25 – $0.80 (retail prop) TOTAL $1.60 – $2.30 per side Round Turn: (enter+exit) = $3.20 – $4.60 per contract Final Notes The Dskyz (DAFE) Adaptive Regime - Quant Machine Pro is more than a strategy—it’s a revolution. Crafted with DAFE’s signature precision, it rises above generic scripts with adaptive regimes, quant-grade signals, and visuals that make trading a thrill. Whether you’re scalping MES or swinging ES, this system empowers you to navigate markets with confidence and style. Join the DAFE crew, light up your charts, and let’s dominate the futures game! (This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.) Use it with discipline. Use it with clarity. Trade smarter. **I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract. Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Estrategia Pine Script®
por DskyzInvestments
Dskyz (DAFE) Aurora Divergence – Quant Master Dskyz (DAFE) Aurora Divergence – Quant Master Introducing the Dskyz (DAFE) Aurora Divergence – Quant Master , a strategy that’s your secret weapon for mastering futures markets like MNQ, NQ, MES, and ES. Born from the legendary Aurora Divergence indicator, this fully automated system transforms raw divergence signals into a quant-grade trading machine, blending precision, risk management, and cyberpunk DAFE visuals that make your charts glow like a neon skyline. Crafted with care and driven by community passion, this strategy stands out in a sea of generic scripts, offering traders a unique edge to outsmart institutional traps and navigate volatile markets. The Aurora Divergence indicator was a cult favorite for spotting price-OBV divergences with its aqua and fuchsia orbs, but traders craved a system to act on those signals with discipline and automation. This strategy delivers, layering advanced filters (z-score, ATR, multi-timeframe, session), dynamic risk controls (kill switches, adaptive stops/TPs), and a real-time dashboard to turn insights into profits. Whether you’re a newbie dipping into futures or a pro hunting reversals, this strat’s got your back with a beginner guide, alerts, and visuals that make trading feel like a sci-fi mission. Let’s dive into every detail and see why this original DAFE creation is a must-have. Why Traders Need This Strategy Futures markets are a battlefield—fast-paced, volatile, and riddled with institutional games that can wipe out undisciplined traders. From the April 28, 2025 NQ 1k-point drop to sneaky ES slippage, the stakes are high. Meanwhile, platforms are flooded with unoriginal, low-effort scripts that promise the moon but deliver noise. The Aurora Divergence – Quant Master rises above, offering: Unmatched Originality: A bespoke system built from the ground up, with custom divergence logic, DAFE visuals, and quant filters that set it apart from copycat clutter. Automation with Precision: Executes trades on divergence signals, eliminating emotional slip-ups and ensuring consistency, even in chaotic sessions. Quant-Grade Filters: Z-score, ATR, multi-timeframe, and session checks filter out noise, targeting high-probability reversals. Robust Risk Management: Daily loss and rolling drawdown kill switches, plus ATR-based stops/TPs, protect your capital like a fortress. Stunning DAFE Visuals: Aqua/fuchsia orbs, aurora bands, and a glowing dashboard make signals intuitive and charts a work of art. Community-Driven: Evolved from trader feedback, this strat’s a labor of love, not a recycled knockoff. Traders need this because it’s a complete, original system that blends accessibility, sophistication, and style. It’s your edge to trade smarter, not harder, in a market full of traps and imitators. 1. Divergence Detection (Core Signal Logic) The strategy’s core is its ability to detect bullish and bearish divergences between price and On-Balance Volume (OBV), pinpointing reversals with surgical accuracy. How It Works: Price Slope: Uses linear regression over a lookback (default: 9 bars) to measure price momentum (priceSlope). OBV Slope: OBV tracks volume flow (+volume if price rises, -volume if falls), with its slope calculated similarly (obvSlope). Bullish Divergence: Price slope negative (falling), OBV slope positive (rising), and price above 50-bar SMA (trend_ma). Bearish Divergence: Price slope positive (rising), OBV slope negative (falling), and price below 50-bar SMA. Smoothing: Requires two consecutive divergence bars (bullDiv2, bearDiv2) to confirm signals, reducing false positives. Strength: Divergence intensity (divStrength = |priceSlope * obvSlope| * sensitivity) is normalized (0–1, divStrengthNorm) for visuals. Why It’s Brilliant: - Divergences catch hidden momentum shifts, often exploited by institutions, giving you an edge on reversals. - The 50-bar SMA filter aligns signals with the broader trend, avoiding choppy markets. - Adjustable lookback (min: 3) and sensitivity (default: 1.0) let you tune for different instruments or timeframes. 2. Filters for Precision Four advanced filters ensure signals are high-probability and market-aligned, cutting through the noise of volatile futures. Z-Score Filter: Logic: Calculates z-score ((close - SMA) / stdev) over a lookback (default: 50 bars). Blocks entries if |z-score| > threshold (default: 1.5) unless disabled (useZFilter = false). Impact: Avoids trades during extreme price moves (e.g., blow-off tops), keeping you in statistically safe zones. ATR Percentile Volatility Filter: Logic: Tracks 14-bar ATR in a 100-bar window (default). Requires current ATR > 80th percentile (percATR) to trade (tradeOk). Impact: Ensures sufficient volatility for meaningful moves, filtering out low-volume chop. Multi-Timeframe (HTF) Trend Filter: Logic: Uses a 50-bar SMA on a higher timeframe (default: 60min). Longs require price > HTF MA (bullTrendOK), shorts < HTF MA (bearTrendOK). Impact: Aligns trades with the bigger trend, reducing counter-trend losses. US Session Filter: Logic: Restricts trading to 9:30am–4:00pm ET (default: enabled, useSession = true) using America/New_York timezone. Impact: Focuses on high-liquidity hours, avoiding overnight spreads and erratic moves. Evolution: - These filters create a robust signal pipeline, ensuring trades are timed for optimal conditions. - Customizable inputs (e.g., zThreshold, atrPercentile) let traders adapt to their style without compromising quality. 3. Risk Management The strategy’s risk controls are a masterclass in balancing aggression and safety, protecting capital in volatile markets. Daily Loss Kill Switch: Logic: Tracks daily loss (dayStartEquity - strategy.equity). Halts trading if loss ≥ $300 (default) and enabled (killSwitch = true, killSwitchActive). Impact: Caps daily downside, crucial during events like April 27, 2025 ES slippage. Rolling Drawdown Kill Switch: Logic: Monitors drawdown (rollingPeak - strategy.equity) over 100 bars (default). Stops trading if > $1000 (rollingKill). Impact: Prevents prolonged losing streaks, preserving capital for better setups. Dynamic Stop-Loss and Take-Profit: Logic: Stops = entry ± ATR * multiplier (default: 1.0x, stopDist). TPs = entry ± ATR * 1.5x (profitDist). Longs: stop below, TP above; shorts: vice versa. Impact: Adapts to volatility, keeping stops tight but realistic, with TPs targeting 1.5:1 reward/risk. Max Bars in Trade: Logic: Closes trades after 8 bars (default) if not already exited. Impact: Frees capital from stagnant trades, maintaining efficiency. Kill Switch Buffer Dashboard: Logic: Shows smallest buffer ($300 - daily loss or $1000 - rolling DD). Displays 0 (red) if kill switch active, else buffer (green). Impact: Real-time risk visibility, letting traders adjust dynamically. Why It’s Brilliant: - Kill switches and ATR-based exits create a safety net, rare in generic scripts. - Customizable risk inputs (maxDailyLoss, dynamicStopMult) suit different account sizes. - Buffer metric empowers disciplined trading, a DAFE signature. 4. Trade Entry and Exit Logic The entry/exit rules are precise, filtered, and adaptive, ensuring trades are deliberate and profitable. Entry Conditions: Long Entry: bullDiv2, cooldown passed (canSignal), ATR filter passed (tradeOk), in US session (inSession), no kill switches (not killSwitchActive, not rollingKill), z-score OK (zOk), HTF trend bullish (bullTrendOK), no existing long (lastDirection != 1, position_size <= 0). Closes shorts first. Short Entry: Same, but for bearDiv2, bearTrendOK, no long (lastDirection != -1, position_size >= 0). Closes longs first. Adaptive Cooldown: Default 2 bars (cooldownBars). Doubles (up to 10) after a losing trade, resets after wins (dynamicCooldown). Exit Conditions: Stop-Loss/Take-Profit: Set per trade (ATR-based). Exits on stop/TP hits. Other Exits: Closes if maxBarsInTrade reached, ATR filter fails, or kill switch activates. Position Management: Ensures no conflicting positions, closing opposites before new entries. Built To Be Reliable and Consistent: - Multi-filtered entries minimize false signals, a stark contrast to basic scripts. - Adaptive cooldown prevents overtrading, especially after losses. - Clean position handling ensures smooth execution, even in fast markets. 5. DAFE Visuals The visuals are a DAFE hallmark, blending function with clean flair to make signals intuitive and charts stunning. Aurora Bands: Display: Bands around price during divergences (bullish: below low, bearish: above high), sized by ATR * bandwidth (default: 0.5). Colors: Aqua (bullish), fuchsia (bearish), with transparency tied to divStrengthNorm. Purpose: Highlights divergence zones with a glowing, futuristic vibe. Divergence Orbs: Display: Large/small circles (aqua below for bullish, fuchsia above for bearish) when bullDiv2/bearDiv2 and canSignal. Labels show strength (0–1). Purpose: Pinpoints entries with eye-catching clarity. Gradient Background: Display: Green (bullish), red (bearish), or gray (neutral), 90–95% transparent. Purpose: Sets the market mood without clutter. Strategy Plots: - Stop/TP Lines: Red (stops), green (TPs) for active trades. - HTF MA: Yellow line for trend context. - Z-Score: Blue step-line (if enabled). - Kill Switch Warning: Red background flash when active. What Makes This Next-Level?: - Visuals make complex signals (divergences, filters) instantly clear, even for beginners. - DAFE’s unique aesthetic (orbs, bands) sets it apart from generic scripts, reinforcing originality. - Functional plots (stops, TPs) enhance trade management. 6. Metrics Dashboard The top-right dashboard (2x8 table) is your command center, delivering real-time insights. Metrics: Daily Loss ($): Current loss vs. day’s start, red if > $300. Rolling DD ($): Drawdown vs. 100-bar peak, red if > $1000. ATR Threshold: Current percATR, green if ATR exceeds, red if not. Z-Score: Current value, green if within threshold, red if not. Signal: “Bullish Div” (aqua), “Bearish Div” (fuchsia), or “None” (gray). Action: “Consider Buying”/“Consider Selling” (signal color) or “Wait” (gray). Kill Switch Buffer ($): Smallest buffer to kill switch, green if > 0, red if 0. Why This Is Important?: - Consolidates critical data, making decisions effortless. - Color-coded metrics guide beginners (e.g., green action = go). - Buffer metric adds transparency, rare in off-the-shelf scripts. 7. Beginner Guide Beginner Guide: Middle-right table (shown once on chart load), explains aqua orbs (bullish, buy) and fuchsia orbs (bearish, sell). Key Features: Futures-Optimized: Tailored for MNQ, NQ, MES, ES with point-value adjustments. Highly Customizable: Inputs for lookback, sensitivity, filters, and risk settings. Real-Time Insights: Dashboard and visuals update every bar. Backtest-Ready: Fixed qty and tick calc for accurate historical testing. User-Friendly: Guide, visuals, and dashboard make it accessible yet powerful. Original Design: DAFE’s unique logic and visuals stand out from generic scripts. How to Use Add to Chart: Load on a 5min MNQ/ES chart in TradingView. Configure Inputs: Adjust instrument, filters, or risk (defaults optimized for MNQ). Monitor Dashboard: Watch signals, actions, and risk metrics (top-right). Backtest: Run in strategy tester to evaluate performance. Live Trade: Connect to a broker (e.g., Tradovate) for automation. Watch for slippage (e.g., April 27, 2025 ES issues). Replay Test: Use bar replay (e.g., April 28, 2025 NQ drop) to test volatility handling. Disclaimer Trading futures involves significant risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Backtest results may not reflect live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred. Backtesting: Frame: 2023-09-20 - 2025-04-29 Fee Typical Range (per side, per contract) CME Exchange $1.14 – $1.20 Clearing $0.10 – $0.30 NFA Regulatory $0.02 Firm/Broker Commis. $0.25 – $0.80 (retail prop) TOTAL $1.60 – $2.30 per side Round Turn: (enter+exit) = $3.20 – $4.60 per contract Final Notes The Dskyz (DAFE) Aurora Divergence – Quant Master isn’t just a strategy—it’s a movement. Crafted with originality and driven by community passion, it rises above the flood of generic scripts to deliver a system that’s as powerful as it is beautiful. With its quant-grade logic, DAFE visuals, and robust risk controls, it empowers traders to tackle futures with confidence and style. Join the DAFE crew, light up your charts, and let’s outsmart the markets together! (This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.) Use it with discipline. Use it with clarity. Trade smarter. **I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract. Created by Dskyz, powered by DAFE Trading Systems. Trade fast, trade bold.
Estrategia Pine Script®
por DskyzInvestments
1
[Sextan] Your Indicator Source PINE v5 MTFLevel: 1 NOTE1: As requested, this is a multiple time frame(MTF) version of input signal source, which enable you to backtest any indicator/strategy MTF with "{Sextan} PINEv4 Sextans Backtest Framework". Courtesy of cheatcountry for his request.security() wrapper in PINE v5 to avoid repainting caused by request.security() function. NOTE2: Many request this indicator template to support PINE v5. Now, here it is .This is ONLY an PINE v5 EXAMPLE on HOW-TO produce a customized "{Sextan} PINEv4 Sextans Backtest Framework" (for bactest framework it does not need to be written by PINE v5)intput signal source, you can define your own indicator in the highlighted area in compliance with the uniform format, which guarantee when you use "Indicator on Indicator" function, it would not produce any error. I use two simple moving average crossings to produce long and short entry signal with SMA3 and SMA8 in the example. Background Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator. Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "death and alive", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report. The main function: The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad. The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script. How to output charts: The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell. The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information: 1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit. 2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close. 3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls. 4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price. 5. In addition to the above signs, there are also text and numbers indicating the profit and loss values ​​of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short. TradingView Strategy Tester Panel: The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades. Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc. Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values. Remarks Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
Indicador Pine Script®
por blackcat1402
[Sextan] Your Indicator Source PINE v4 MTFLevel: 1 NOTE1: As requested, this is a multiple time frame(MTF) version of input signal source, which enable you to backtest any indicator/strategy MTF with "{Sextan} PINEv4 Sextans Backtest Framework". Courtesy of cheatcountry for his security() wrapper to avoid repainting caused by security() function. NOTE2: This is ONLY an EXAMPLE on HOW-TO produce a customized "{Sextan} PINEv4 Sextans Backtest Framework" intput signal source, you can define your own indicator in the highlighted area in compliance with the uniform format, which guarantee when you use "Indicator on Indicator" function, it would not produce any error. I use two simple moving average crossings to produce long and short entry signal with SMA3 and SMA8 in the example. Background Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator. Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "death and alive", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report. The main function: The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad. The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script. How to output charts: The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell. The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information: 1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit. 2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close. 3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls. 4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price. 5. In addition to the above signs, there are also text and numbers indicating the profit and loss values ​​of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short. TradingView Strategy Tester Panel: The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades. Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc. Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values. Remarks Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
Indicador Pine Script®
por blackcat1402
1
Trading The Loonie (CADUSD)A port of the trading strategy described at technical.traders.com "In “Trading The Loonie,” which appeared in the December 2015 issue of STOCKS & COMMODITIES, author Markos Katsanos explains the heavy correlation between the Canadian dollar and crude oil. He then goes on to describe how one could trade this correlation. Using similar logic as that employed in Bollinger Bands, Katsanos has built a study to provide buy and sell signals for trading the Canadian dollar future." See Also: - Backtesting and forwardtesting (of TradingView Strategies) - 9 Mistakes Quants Make that Cause Backtests to Lie (blog.quantopian.com) - When Backtests Meet Reality (financial-hacker.com) - Why MT4 backtesting does not work (www.stevehopwoodforex.com)
Estrategia Pine Script®
por sherwind
Liquid Pulse Liquid Pulse by Dskyz (DAFE) Trading Systems Liquid Pulse is a trading algo built by Dskyz (DAFE) Trading Systems for futures markets like NQ1!, designed to snag high-probability trades with tight risk control. it fuses a confluence system—VWAP, MACD, ADX, volume, and liquidity sweeps—with a trade scoring setup, daily limits, and VIX pauses to dodge wild volatility. visuals include simple signals, VWAP bands, and a dashboard with stats. Core Components for Liquid Pulse Volume Sensitivity (volumeSensitivity) controls how much volume spikes matter for entries. options: 'Low', 'Medium', 'High' default: 'High' (catches small spikes, good for active markets) tweak it: 'Low' for calm markets, 'High' for chaos. MACD Speed (macdSpeed) sets the MACD’s pace for momentum. options: 'Fast', 'Medium', 'Slow' default: 'Medium' (solid balance) tweak it: 'Fast' for scalping, 'Slow' for swings. Daily Trade Limit (dailyTradeLimit) caps trades per day to keep risk in check. range: 1 to 30 default: 20 tweak it: 5-10 for safety, 20-30 for action. Number of Contracts (numContracts) sets position size. range: 1 to 20 default: 4 tweak it: up for big accounts, down for small. VIX Pause Level (vixPauseLevel) stops trading if VIX gets too hot. range: 10 to 80 default: 39.0 tweak it: 30 to avoid volatility, 50 to ride it. Min Confluence Conditions (minConditions) sets how many signals must align. range: 1 to 5 default: 2 tweak it: 3-4 for strict, 1-2 for more trades. Min Trade Score (Longs/Shorts) (minTradeScoreLongs/minTradeScoreShorts) filters trade quality. longs range: 0 to 100 default: 73 shorts range: 0 to 100 default: 75 tweak it: 80-90 for quality, 60-70 for volume. Liquidity Sweep Strength (sweepStrength) gauges breakouts. range: 0.1 to 1.0 default: 0.5 tweak it: 0.7-1.0 for strong moves, 0.3-0.5 for small. ADX Trend Threshold (adxTrendThreshold) confirms trends. range: 10 to 100 default: 41 tweak it: 40-50 for trends, 30-35 for weak ones. ADX Chop Threshold (adxChopThreshold) avoids chop. range: 5 to 50 default: 20 tweak it: 15-20 to dodge chop, 25-30 to loosen. VWAP Timeframe (vwapTimeframe) sets VWAP period. options: '15', '30', '60', '240', 'D' default: '60' (1-hour) tweak it: 60 for day, 240 for swing, D for long. Take Profit Ticks (Longs/Shorts) (takeProfitTicksLongs/takeProfitTicksShorts) sets profit targets. longs range: 5 to 100 default: 25.0 shorts range: 5 to 100 default: 20.0 tweak it: 30-50 for trends, 10-20 for chop. Max Profit Ticks (maxProfitTicks) caps max gain. range: 10 to 200 default: 60.0 tweak it: 80-100 for big moves, 40-60 for tight. Min Profit Ticks to Trail (minProfitTicksTrail) triggers trailing. range: 1 to 50 default: 7.0 tweak it: 10-15 for big gains, 5-7 for quick locks. Trailing Stop Ticks (trailTicks) sets trail distance. range: 1 to 50 default: 5.0 tweak it: 8-10 for room, 3-5 for fast locks. Trailing Offset Ticks (trailOffsetTicks) sets trail offset. range: 1 to 20 default: 2.0 tweak it: 1-2 for tight, 5-10 for loose. ATR Period (atrPeriod) measures volatility. range: 5 to 50 default: 9 tweak it: 14-20 for smooth, 5-9 for reactive. Hardcoded Settings volLookback: 30 ('Low'), 20 ('Medium'), 11 ('High') volThreshold: 1.5 ('Low'), 1.8 ('Medium'), 2 ('High') swingLen: 5 Execution Logic Overview trades trigger when confluence conditions align, entering long or short with set position sizes. exits use dynamic take-profits, trailing stops after a profit threshold, hard stops via ATR, and a time stop after 100 bars. Features Multi-Signal Confluence: needs VWAP, MACD, volume, sweeps, and ADX to line up. Risk Control: ATR-based stops (capped 15 ticks), take-profits (scaled by volatility), and trails. Market Filters: VIX pause, ADX trend/chop checks, volatility gates. Dashboard: shows scores, VIX, ADX, P/L, win %, streak. Visuals Simple signals (green up triangles for longs, red down for shorts) and VWAP bands with glow. info table (bottom right) with MACD momentum. dashboard (top right) with stats. Chart and Backtest: NQ1! futures, 5-minute chart. works best in trending, volatile conditions. tweak inputs for other markets—test thoroughly. Backtesting: NQ1! Frame: Jan 19, 2025, 09:00 — May 02, 2025, 16:00 Slippage: 3 Commission: $4.60 Fee Typical Range (per side, per contract) CME Exchange $1.14 – $1.20 Clearing $0.10 – $0.30 NFA Regulatory $0.02 Firm/Broker Commis. $0.25 – $0.80 (retail prop) TOTAL $1.60 – $2.30 per side Round Turn: (enter+exit) = $3.20 – $4.60 per contract Disclaimer this is for education only. past results don’t predict future wins. trading’s risky—only use money you can lose. backtest and validate before going live. (expect moderators to nitpick some random chart symbol rule—i’ll fix and repost if they pull it.) About the Author Dskyz (DAFE) Trading Systems crafts killer trading algos. Liquid Pulse is pure research and grit, built for smart, bold trading. Use it with discipline. Use it with clarity. Trade smarter. I’ll keep dropping badass strategies ‘til i build a brand or someone signs me up. 2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Estrategia Pine Script®
por DskyzInvestments
1
Supertrend Advance Pullback StrategyHandbook for the Supertrend Advance Strategy 1. Introduction Purpose of the Handbook: The main purpose of this handbook is to serve as a comprehensive guide for traders and investors who are looking to explore and harness the potential of the Supertrend Advance Strategy. In the rapidly changing financial market, having the right tools and strategies at one's disposal is crucial. Whether you're a beginner hoping to dive into the world of trading or a seasoned investor aiming to optimize and diversify your portfolio, this handbook offers the insights and methodologies you need. By the end of this guide, readers should have a clear understanding of how the Supertrend Advance Strategy works, its benefits, potential pitfalls, and practical application in various trading scenarios. Overview of the Supertrend Advance Pullback Strategy: At its core, the Supertrend Advance Strategy is an evolution of the popular Supertrend Indicator. Designed to generate buy and sell signals in trending markets, the Supertrend Indicator has been a favorite tool for many traders around the world. The Advance Strategy, however, builds upon this foundation by introducing enhanced mechanisms, filters, and methodologies to increase precision and reduce false signals. 1. Basic Concept: The Supertrend Advance Strategy relies on a combination of price action and volatility to determine the potential trend direction. By assessing the average true range (ATR) in conjunction with specific price points, this strategy aims to highlight the potential starting and ending points of market trends. 2. Methodology: Unlike the traditional Supertrend Indicator, which primarily focuses on closing prices and ATR, the Advance Strategy integrates other critical market variables, such as volume, momentum oscillators, and perhaps even fundamental data, to validate its signals. This multidimensional approach ensures that the generated signals are more reliable and are less prone to market noise. 3. Benefits: One of the main benefits of the Supertrend Advance Strategy is its ability to filter out false breakouts and minor price fluctuations, which can often lead to premature exits or entries in the market. By waiting for a confluence of factors to align, traders using this advanced strategy can increase their chances of entering or exiting trades at optimal points. 4. Practical Applications: The Supertrend Advance Strategy can be applied across various timeframes, from intraday trading to swing trading and even long-term investment scenarios. Furthermore, its flexible nature allows it to be tailored to different asset classes, be it stocks, commodities, forex, or cryptocurrencies. In the subsequent sections of this handbook, we will delve deeper into the intricacies of this strategy, offering step-by-step guidelines on its application, case studies, and tips for maximizing its efficacy in the volatile world of trading. As you journey through this handbook, we encourage you to approach the Supertrend Advance Strategy with an open mind, testing and tweaking it as per your personal trading style and risk appetite. The ultimate goal is not just to provide you with a new tool but to empower you with a holistic strategy that can enhance your trading endeavors. 2. Getting Started Navigating the financial markets can be a daunting task without the right tools. This section is dedicated to helping you set up the Supertrend Advance Strategy on one of the most popular charting platforms, TradingView. By following the steps below, you'll be able to integrate this strategy into your charts and start leveraging its insights in no time. Setting up on TradingView: TradingView is a web-based platform that offers a wide range of charting tools, social networking, and market data. Before you can apply the Supertrend Advance Strategy, you'll first need a TradingView account. If you haven't set one up yet, here's how: 1. Account Creation: • Visit TradingView's official website. • Click on the "Join for free" or "Sign up" button. • Follow the registration process, providing the necessary details and setting up your login credentials. 2. Navigating the Dashboard: • Once logged in, you'll be taken to your dashboard. Here, you'll see a variety of tools, including watchlists, alerts, and the main charting window. • To begin charting, type in the name or ticker of the asset you're interested in the search bar at the top. 3. Configuring Chart Settings: • Before integrating the Supertrend Advance Strategy, familiarize yourself with the chart settings. This can be accessed by clicking the 'gear' icon on the top right of the chart window. • Adjust the chart type, time intervals, and other display settings to your preference. Integrating the Strategy into a Chart: Now that you're set up on TradingView, it's time to integrate the Supertrend Advance Strategy. 1. Accessing the Pine Script Editor: • Located at the top-center of your screen, you'll find the "Pine Editor" tab. Click on it. • This is where custom strategies and indicators are scripted or imported. 2. Loading the Supertrend Advance Strategy Script: • Depending on whether you have the script or need to find it, there are two paths: • If you have the script: Copy the Supertrend Advance Strategy script, and then paste it into the Pine Editor. • If searching for the script: Click on the “Indicators” icon (looks like a flame) at the top of your screen, and then type “Supertrend Advance Strategy” in the search bar. If available, it will show up in the list. Simply click to add it to your chart. 3. Applying the Strategy: • After pasting or selecting the Supertrend Advance Strategy in the Pine Editor, click on the “Add to Chart” button located at the top of the editor. This will overlay the strategy onto your main chart window. 4. Configuring Strategy Settings: • Once the strategy is on your chart, you'll notice a small settings ('gear') icon next to its name in the top-left of the chart window. Click on this to access settings. • Here, you can adjust various parameters of the Supertrend Advance Strategy to better fit your trading style or the specific asset you're analyzing. 5. Interpreting Signals: • With the strategy applied, you'll now see buy/sell signals represented on your chart. Take time to familiarize yourself with how these look and behave over various timeframes and market conditions. 3. Strategy Overview What is the Supertrend Advance Strategy? The Supertrend Advance Strategy is a refined version of the classic Supertrend Indicator, which was developed to aid traders in spotting market trends. The strategy utilizes a combination of data points, including average true range (ATR) and price momentum, to generate buy and sell signals. In essence, the Supertrend Advance Strategy can be visualized as a line that moves with the price. When the price is above the Supertrend line, it indicates an uptrend and suggests a potential buy position. Conversely, when the price is below the Supertrend line, it hints at a downtrend, suggesting a potential selling point. Strategy Goals and Objectives: 1. Trend Identification: At the core of the Supertrend Advance Strategy is the goal to efficiently and consistently identify prevailing market trends. By recognizing these trends, traders can position themselves to capitalize on price movements in their favor. 2. Reducing Noise: Financial markets are often inundated with 'noise' - short-term price fluctuations that can mislead traders. The Supertrend Advance Strategy aims to filter out this noise, allowing for clearer decision-making. 3. Enhancing Risk Management: With clear buy and sell signals, traders can set more precise stop-loss and take-profit points. This leads to better risk management and potentially improved profitability. 4. Versatility: While primarily used for trend identification, the strategy can be integrated with other technical tools and indicators to create a comprehensive trading system. Type of Assets/Markets to Apply the Strategy: 1. Equities: The Supertrend Advance Strategy is highly popular among stock traders. Its ability to capture long-term trends makes it particularly useful for those trading individual stocks or equity indices. 2. Forex: Given the 24-hour nature of the Forex market and its propensity for trends, the Supertrend Advance Strategy is a valuable tool for currency traders. 3. Commodities: Whether it's gold, oil, or agricultural products, commodities often move in extended trends. The strategy can help in identifying and capitalizing on these movements. 4. Cryptocurrencies: The volatile nature of cryptocurrencies means they can have pronounced trends. The Supertrend Advance Strategy can aid crypto traders in navigating these often tumultuous waters. 5. Futures & Options: Traders and investors in derivative markets can utilize the strategy to make more informed decisions about contract entries and exits. It's important to note that while the Supertrend Advance Strategy can be applied across various assets and markets, its effectiveness might vary based on market conditions, timeframe, and the specific characteristics of the asset in question. As always, it's recommended to use the strategy in conjunction with other analytical tools and to backtest its effectiveness in specific scenarios before committing to trades. 4. Input Settings Understanding and correctly configuring input settings is crucial for optimizing the Supertrend Advance Strategy for any specific market or asset. These settings, when tweaked correctly, can drastically impact the strategy's performance. Grouping Inputs: Before diving into individual input settings, it's important to group similar inputs. Grouping can simplify the user interface, making it easier to adjust settings related to a specific function or indicator. Strategy Choice: This input allows traders to select from various strategies that incorporate the Supertrend indicator. Options might include "Supertrend with RSI," "Supertrend with MACD," etc. By choosing a strategy, the associated input settings for that strategy become available. Supertrend Settings: 1. Multiplier: Typically, a default value of 3 is used. This multiplier is used in the ATR calculation. Increasing it makes the Supertrend line further from prices, while decreasing it brings the line closer. 2. Period: The number of bars used in the ATR calculation. A common default is 7. EMA Settings (Exponential Moving Average): 1. Period: Defines the number of previous bars used to calculate the EMA. Common periods are 9, 21, 50, and 200. 2. Source: Allows traders to choose which price (Open, Close, High, Low) to use in the EMA calculation. RSI Settings (Relative Strength Index): 1. Length: Determines how many periods are used for RSI calculation. The standard setting is 14. 2. Overbought Level: The threshold at which the asset is considered overbought, typically set at 70. 3. Oversold Level: The threshold at which the asset is considered oversold, often at 30. MACD Settings (Moving Average Convergence Divergence): 1. Short Period: The shorter EMA, usually set to 12. 2. Long Period: The longer EMA, commonly set to 26. 3. Signal Period: Defines the EMA of the MACD line, typically set at 9. CCI Settings (Commodity Channel Index): 1. Period: The number of bars used in the CCI calculation, often set to 20. 2. Overbought Level: Typically set at +100, denoting overbought conditions. 3. Oversold Level: Usually set at -100, indicating oversold conditions. SL/TP Settings (Stop Loss/Take Profit): 1. SL Multiplier: Defines the multiplier for the average true range (ATR) to set the stop loss. 2. TP Multiplier: Defines the multiplier for the average true range (ATR) to set the take profit. Filtering Conditions: This section allows traders to set conditions to filter out certain signals. For example, one might only want to take buy signals when the RSI is below 30, ensuring they buy during oversold conditions. Trade Direction and Backtest Period: 1. Trade Direction: Allows traders to specify whether they want to take long trades, short trades, or both. 2. Backtest Period: Specifies the time range for backtesting the strategy. Traders can choose from options like 'Last 6 months,' 'Last 1 year,' etc. It's essential to remember that while default settings are provided for many of these tools, optimal settings can vary based on the market, timeframe, and trading style. Always backtest new settings on historical data to gauge their potential efficacy. 5. Understanding Strategy Conditions Developing an understanding of the conditions set within a trading strategy is essential for traders to maximize its potential. Here, we delve deep into the logic behind these conditions, using the Supertrend Advance Strategy as our focal point. Basic Logic Behind Conditions: Every strategy is built around a set of conditions that provide buy or sell signals. The conditions are based on mathematical or statistical methods and are rooted in the study of historical price data. The fundamental idea is to recognize patterns or behaviors that have been profitable in the past and might be profitable in the future. Buy and Sell Conditions: 1. Buy Conditions: Usually formulated around bullish signals or indicators suggesting upward price momentum. 2. Sell Conditions: Centered on bearish signals or indicators indicating downward price momentum. Simple Strategy: The simple strategy could involve using just the Supertrend indicator. Here: • Buy: When price closes above the Supertrend line. • Sell: When price closes below the Supertrend line. Pullback Strategy: This strategy capitalizes on price retracements: • Buy: When the price retraces to the Supertrend line after a bullish signal and is supported by another bullish indicator. • Sell: When the price retraces to the Supertrend line after a bearish signal and is confirmed by another bearish indicator. Indicators Used: EMA (Exponential Moving Average): • Logic: EMA gives more weight to recent prices, making it more responsive to current price movements. A shorter-period EMA crossing above a longer-period EMA can be a bullish sign, while the opposite is bearish. RSI (Relative Strength Index): • Logic: RSI measures the magnitude of recent price changes to analyze overbought or oversold conditions. Values above 70 are typically considered overbought, and values below 30 are considered oversold. MACD (Moving Average Convergence Divergence): • Logic: MACD assesses the relationship between two EMAs of a security’s price. The MACD line crossing above the signal line can be a bullish signal, while crossing below can be bearish. CCI (Commodity Channel Index): • Logic: CCI compares a security's average price change with its average price variation. A CCI value above +100 may mean the price is overbought, while below -100 might signify an oversold condition. And others... As the strategy expands or contracts, more indicators might be added or removed. The crucial point is to understand the core logic behind each, ensuring they align with the strategy's objectives. Logic Behind Each Indicator: 1. EMA: Emphasizes recent price movements; provides dynamic support and resistance levels. 2. RSI: Indicates overbought and oversold conditions based on recent price changes. 3. MACD: Showcases momentum and direction of a trend by comparing two EMAs. 4. CCI: Measures the difference between a security's price change and its average price change. Understanding strategy conditions is not just about knowing when to buy or sell but also about comprehending the underlying market dynamics that those conditions represent. As you familiarize yourself with each condition and indicator, you'll be better prepared to adapt and evolve with the ever-changing financial markets. 6. Trade Execution and Management Trade execution and management are crucial aspects of any trading strategy. Efficient execution can significantly impact profitability, while effective management can preserve capital during adverse market conditions. In this section, we'll explore the nuances of position entry, exit strategies, and various Stop Loss (SL) and Take Profit (TP) methodologies within the Supertrend Advance Strategy. Position Entry: Effective trade entry revolves around: 1. Timing: Enter at a point where the risk-reward ratio is favorable. This often corresponds to confirmatory signals from multiple indicators. 2. Volume Analysis: Ensure there's adequate volume to support the movement. Volume can validate the strength of a signal. 3. Confirmation: Use multiple indicators or chart patterns to confirm the entry point. For instance, a buy signal from the Supertrend indicator can be confirmed with a bullish MACD crossover. Position Exit Strategies: A successful exit strategy will lock in profits and minimize losses. Here are some strategies: 1. Fixed Time Exit: Exiting after a predetermined period. 2. Percentage-based Profit Target: Exiting after a certain percentage gain. 3. Indicator-based Exit: Exiting when an indicator gives an opposing signal. Percentage-based SL/TP: • Stop Loss (SL): Set a fixed percentage below the entry price to limit potential losses. • Example: A 2% SL on an entry at $100 would trigger a sell at $98. • Take Profit (TP): Set a fixed percentage above the entry price to lock in gains. • Example: A 5% TP on an entry at $100 would trigger a sell at $105. Supertrend-based SL/TP: • Stop Loss (SL): Position the SL at the Supertrend line. If the price breaches this line, it could indicate a trend reversal. • Take Profit (TP): One could set the TP at a point where the Supertrend line flattens or turns, indicating a possible slowdown in momentum. Swing high/low-based SL/TP: • Stop Loss (SL): For a long position, set the SL just below the recent swing low. For a short position, set it just above the recent swing high. • Take Profit (TP): For a long position, set the TP near a recent swing high or resistance. For a short position, near a swing low or support. And other methods... 1. Trailing Stop Loss: This dynamic SL adjusts with the price movement, locking in profits as the trade moves in your favor. 2. Multiple Take Profits: Divide the position into segments and set multiple TP levels, securing profits in stages. 3. Opposite Signal Exit: Exit when another reliable indicator gives an opposite signal. Trade execution and management are as much an art as they are a science. They require a blend of analytical skill, discipline, and intuition. Regularly reviewing and refining your strategies, especially in light of changing market conditions, is crucial to maintaining consistent trading performance. 7. Visual Representations Visual tools are essential for traders, as they simplify complex data into an easily interpretable format. Properly analyzing and understanding the plots on a chart can provide actionable insights and a more intuitive grasp of market conditions. In this section, we’ll delve into various visual representations used in the Supertrend Advance Strategy and their significance. Understanding Plots on the Chart: Charts are the primary visual aids for traders. The arrangement of data points, lines, and colors on them tell a story about the market's past, present, and potential future moves. 1. Data Points: These represent individual price actions over a specific timeframe. For instance, a daily chart will have data points showing the opening, closing, high, and low prices for each day. 2. Colors: Used to indicate the nature of price movement. Commonly, green is used for bullish (upward) moves and red for bearish (downward) moves. Trend Lines: Trend lines are straight lines drawn on a chart that connect a series of price points. Their significance: 1. Uptrend Line: Drawn along the lows, representing support. A break below might indicate a trend reversal. 2. Downtrend Line: Drawn along the highs, indicating resistance. A break above might suggest the start of a bullish trend. Filled Areas: These represent a range between two values on a chart, usually shaded or colored. For instance: 1. Bollinger Bands: The area between the upper and lower band is filled, giving a visual representation of volatility. 2. Volume Profile: Can show a filled area representing the amount of trading activity at different price levels. Stop Loss and Take Profit Lines: These are horizontal lines representing pre-determined exit points for trades. 1. Stop Loss Line: Indicates the level at which a trade will be automatically closed to limit losses. Positioned according to the trader's risk tolerance. 2. Take Profit Line: Denotes the target level to lock in profits. Set according to potential resistance (for long trades) or support (for short trades) or other technical factors. Trailing Stop Lines: A trailing stop is a dynamic form of stop loss that moves with the price. On a chart: 1. For Long Trades: Starts below the entry price and moves up with the price but remains static if the price falls, ensuring profits are locked in. 2. For Short Trades: Starts above the entry price and moves down with the price but remains static if the price rises. Visual representations offer traders a clear, organized view of market dynamics. Familiarity with these tools ensures that traders can quickly and accurately interpret chart data, leading to more informed decision-making. Always ensure that the visual aids used resonate with your trading style and strategy for the best results. 8. Backtesting Backtesting is a fundamental process in strategy development, enabling traders to evaluate the efficacy of their strategy using historical data. It provides a snapshot of how the strategy would have performed in past market conditions, offering insights into its potential strengths and vulnerabilities. In this section, we'll explore the intricacies of setting up and analyzing backtest results and the caveats one must be aware of. Setting Up Backtest Period: 1. Duration: Determine the timeframe for the backtest. It should be long enough to capture various market conditions (bullish, bearish, sideways). For instance, if you're testing a daily strategy, consider a period of several years. 2. Data Quality: Ensure the data source is reliable, offering high-resolution and clean data. This is vital to get accurate backtest results. 3. Segmentation: Instead of a continuous period, sometimes it's helpful to backtest over distinct market phases, like a particular bear or bull market, to see how the strategy holds up in different environments. Analyzing Backtest Results: 1. Performance Metrics: Examine metrics like the total return, annualized return, maximum drawdown, Sharpe ratio, and others to gauge the strategy's efficiency. 2. Win Rate: It's the ratio of winning trades to total trades. A high win rate doesn't always signify a good strategy; it should be evaluated in conjunction with other metrics. 3. Risk/Reward: Understand the average profit versus the average loss per trade. A strategy might have a low win rate but still be profitable if the average gain far exceeds the average loss. 4. Drawdown Analysis: Review the periods of losses the strategy could incur and how long it takes, on average, to recover. 9. Tips and Best Practices Successful trading requires more than just knowing how a strategy works. It necessitates an understanding of when to apply it, how to adjust it to varying market conditions, and the wisdom to recognize and avoid common pitfalls. This section offers insightful tips and best practices to enhance the application of the Supertrend Advance Strategy. When to Use the Strategy: 1. Market Conditions: Ideally, employ the Supertrend Advance Strategy during trending market conditions. This strategy thrives when there are clear upward or downward trends. It might be less effective during consolidative or sideways markets. 2. News Events: Be cautious around significant news events, as they can cause extreme volatility. It might be wise to avoid trading immediately before and after high-impact news. 3. Liquidity: Ensure you are trading in assets/markets with sufficient liquidity. High liquidity ensures that the price movements are more reflective of genuine market sentiment and not due to thin volume. Adjusting Settings for Different Markets/Timeframes: 1. Markets: Each market (stocks, forex, commodities) has its own characteristics. It's essential to adjust the strategy's parameters to align with the market's volatility and liquidity. 2. Timeframes: Shorter timeframes (like 1-minute or 5-minute charts) tend to have more noise. You might need to adjust the settings to filter out false signals. Conversely, for longer timeframes (like daily or weekly charts), you might need to be more responsive to genuine trend changes. 3. Customization: Regularly review and tweak the strategy's settings. Periodic adjustments can ensure the strategy remains optimized for the current market conditions. 10. Frequently Asked Questions (FAQs) Given the complexities and nuances of the Supertrend Advance Strategy, it's only natural for traders, both new and seasoned, to have questions. This section addresses some of the most commonly asked questions regarding the strategy. 1. What exactly is the Supertrend Advance Strategy? The Supertrend Advance Strategy is an evolved version of the traditional Supertrend indicator. It's designed to provide clearer buy and sell signals by incorporating additional indicators like EMA, RSI, MACD, CCI, etc. The strategy aims to capitalize on market trends while minimizing false signals. 2. Can I use the Supertrend Advance Strategy for all asset types? Yes, the strategy can be applied to various asset types like stocks, forex, commodities, and cryptocurrencies. However, it's crucial to adjust the settings accordingly to suit the specific characteristics and volatility of each asset type. 3. Is this strategy suitable for day trading? Absolutely! The Supertrend Advance Strategy can be adjusted to suit various timeframes, making it versatile for both day trading and long-term trading. Remember to fine-tune the settings to align with the timeframe you're trading on. 4. How do I deal with false signals? No strategy is immune to false signals. However, by combining the Supertrend with other indicators and adhering to strict risk management protocols, you can minimize the impact of false signals. Always use stop-loss orders and consider filtering trades with additional confirmation signals. 5. Do I need any prior trading experience to use this strategy? While the Supertrend Advance Strategy is designed to be user-friendly, having a foundational understanding of trading and market analysis can greatly enhance your ability to employ the strategy effectively. If you're a beginner, consider pairing the strategy with further education and practice on demo accounts. 6. How often should I review and adjust the strategy settings? There's no one-size-fits-all answer. Some traders adjust settings weekly, while others might do it monthly. The key is to remain responsive to changing market conditions. Regular backtesting can give insights into potential required adjustments. 7. Can the Supertrend Advance Strategy be automated? Yes, many traders use algorithmic trading platforms to automate their strategies, including the Supertrend Advance Strategy. However, always monitor automated systems regularly to ensure they're operating as intended. 8. Are there any markets or conditions where the strategy shouldn't be used? The strategy might generate more false signals in markets that are consolidative or range-bound. During significant news events or times of unexpected high volatility, it's advisable to tread with caution or stay out of the market. 9. How important is backtesting with this strategy? Backtesting is crucial as it allows traders to understand how the strategy would have performed in the past, offering insights into potential profitability and areas of improvement. Always backtest any new setting or tweak before applying it to live trades. 10. What if the strategy isn't working for me? No strategy guarantees consistent profits. If it's not working for you, consider reviewing your settings, seeking expert advice, or complementing the Supertrend Advance Strategy with other analysis methods. Remember, continuous learning and adaptation are the keys to trading success. Other comments Value of combining several indicators in this script and how they work together Diversification of Signals: Just as diversifying an investment portfolio can reduce risk, using multiple indicators can offer varied perspectives on potential price movements. Each indicator can capture a different facet of the market, ensuring that traders are not overly reliant on a single data point. Confirmation & Reduced False Signals: A common challenge with many indicators is the potential for false signals. By requiring confirmation from multiple indicators before acting, the chances of acting on a false signal can be significantly reduced. Flexibility Across Market Conditions: Different indicators might perform better under different market conditions. For example, while moving averages might excel in trending markets, oscillators like RSI might be more useful during sideways or range-bound conditions. A mashup strategy can potentially adapt better to varying market scenarios. Comprehensive Analysis: With multiple indicators, traders can gauge trend strength, momentum, volatility, and potential market reversals all at once, providing a holistic view of the market. How do the different indicators in the Supertrend Advance Strategy work together? Supertrend: This is primarily a trend-following indicator. It provides traders with buy and sell signals based on the volatility of the price. When combined with other indicators, it can filter out noise and give more weight to strong, confirmed trends. EMA (Exponential Moving Average): EMA gives more weight to recent price data. It can be used to identify the direction and strength of a trend. When the price is above the EMA, it's generally considered bullish, and vice versa. RSI (Relative Strength Index): An oscillator that measures the magnitude of recent price changes to evaluate overbought or oversold conditions. By cross-referencing with other indicators like EMA or MACD, traders can spot potential reversals or confirmations of a trend. MACD (Moving Average Convergence Divergence): This indicator identifies changes in the strength, direction, momentum, and duration of a trend in a stock's price. When the MACD line crosses above the signal line, it can be a bullish sign, and when it crosses below, it can be bearish. Pairing MACD with Supertrend can provide dual confirmation of a trend. CCI (Commodity Channel Index): Initially developed for commodities, CCI can indicate overbought or oversold conditions. It can be used in conjunction with other indicators to determine entry and exit points. In essence, the synergy of these indicators provides a balanced, comprehensive approach to trading. Each indicator offers its unique lens into market conditions, and when they align, it can be a powerful indication of a trading opportunity. This combination not only reduces the potential drawbacks of each individual indicator but leverages their strengths, aiming for more consistent and informed trading decisions. Backtesting and Default Settings • This indicator has been optimized to be applied for 1 hour-charts. However, the underlying principles of this strategy are supply and demand in the financial markets and the strategy can be applied to all timeframes. Daytraders can use the 1min- or 5min charts, swing-traders can use the daily charts. • This strategy has been designed to identify the most promising, highest probability entries and trades for each stock or other financial security. • The combination of the qualifiers results in a highly selective strategy which only considers the most promising swing-trading entries. As a result, you will normally only find a low number of trades for each stock or other financial security per year in case you apply this strategy for the daily charts. Shorter timeframes will result in a higher number of trades / year. • Consequently, traders need to apply this strategy for a full watchlist rather than just one financial security. • Default properties: RSI on (length 14, RSI buy level 50, sell level 50), EMA, RSI, MACD on, type of strategy pullback, SL/TP type: ATR (length 10, factor 3), trade direction both, quantity 5, take profit swing hl 5.1, highest / lowest lookback 2, enable ATR trail (ATR length 10, SL ATR multiplier 1.4, TP multiplier 2.1, lookback = 4, trade direction = both).
Estrategia Pine Script®
por JS_TechTrading
1
[Sextan] Your Indicator Source for PINE v5Level: 1 NOTE: Many request this indicator template to support PINE v5. Now, here it is .This is ONLY an PINE v5 EXAMPLE on HOW-TO produce a customized "{Sextan} PINEv4 Sextans Backtest Framework" (for bactest framework it does not need to be written by PINE v5)intput signal source, you can define your own indicator in the highlighted area in compliance with the uniform format, which guarantee when you use "Indicator on Indicator" function, it would not produce any error. I use two simple moving average crossings to produce long and short entry signal with SMA3 and SMA8 in the example. Background Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator. Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "death and alive", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report. The main function: The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad. The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script. How to output charts: The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell. The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information: 1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit. 2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close. 3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls. 4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price. 5. In addition to the above signs, there are also text and numbers indicating the profit and loss values ​​of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short. TradingView Strategy Tester Panel: The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades. Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc. Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values. Remarks Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
Indicador Pine Script®
por blackcat1402
Intraday Session Levels: Pre-Mkt, 5m, 15m (Replay/Toggle/Labels)Intraday Session Levels: Pre-Mkt, 5m, 15m (Replay/Toggle/Labels) Version v1.0 Live session levels for every trader! This indicator automatically tracks and draws the most actionable intraday levels as they develop—live in real-time and fully compatible with TradingView’s bar replay and backtesting. How it works: Pre-Market High & Low: Levels appear and update live as soon as the pre-market session starts (4:00am ET), then “freeze” at the official open (9:30am ET) and remain visible for the rest of the day. First 5-Minute Candle High/Low: Drawn instantly after the first 5-minute candle (9:30–9:35am ET) completes. First 15-Minute Candle High/Low: Drawn right after the first 15-minute candle (9:30–9:45am ET) completes. Labels on every line Each level is clearly labeled on your chart (“PreMkt High”, “5m Low”, “15m High”, etc). Perfect for backtesting: All levels display exactly as they would have appeared in real time, making this indicator fully bar replay and historical test compatible. Flexible ON/OFF toggles: Instantly show or hide Pre-Mkt, 5m, and 15m levels via the settings panel. Why use it? Identify support/resistance and key reaction zones intraday Fade or break the opening range with confidence Backtest your strategies with accurate historical context Reduce chart clutter with customizable, minimal visuals Whether you’re a scalper, day trader, or backtest enthusiast, this tool keeps your charts focused and your edge sharp. Developed by
Indicador Pine Script®
por nvijaybhargav77
Breakout Trend Trading Strategy - V1Strategy in nutshell: This strategy is made to be used in daily time-frames. Works better on trending instruments where volume is available. Hence, this is more suitable for trending shares rather than currencies, commodities and indexes where volume data is either not present or not reliable. Breakout signifies the continuation of trend. Hence, trade in the direction of breakouts. Breakouts are calculated based on high volume and price movement in a day. This will be combined with few other conditions to generate buy and sell signals along with stop and compound targets. Supertrend is used for trend bias. Our buy and sell targets do not directly depend on the bias. But, entry criteria in opposite trend is made much difficult than that of trend direction. Further explanation of method and input parameters are explained below. Backtesting parameters : Capital and position sizing : Capital and position sizing parameters are set to test investing 2000 wholly on certain stock without compounding. Initial Capital : 2000 Order Size : 100% of equity Pyramiding : 1 ExitOnSignal : If unchecked exit is triggered solely on trailing stop Trade Direction : Long, Short or All. Short condition is riskier than long conditions and often results in losses as per my observation. On most of the stocks trending up, strategy will not generate any short signals. This is achieved by comparing yearly high lows to previous two years to decide whether to allow short or long entries. allowImmediateCompound : Applicable only if compounding/pyramiding is enabled in trade. If checked allows to place compounding orders immediately. If unchecked, it waits for stopline to cross order price before placing next compound. Display Mode : Targets : Whenever breakout happens, show marker for upTarget and downTarget TargetChannel : Show up target and downtarget as a channel Target With Stop : Along with targets, show also stop levels for breakouts Up Channel : Channel created from UpTarget and respective stops Down Channel : Channel created from DownTarget and respective stops ShowTrailingStop : Shows trailing stop and compound lines when there is a trading position. ShowTargetLevels : Shows Buy Sell target levels along with stop and compound lines. Trades are done as market orders. Hence, target levels are displayed after strategy makes the trade. Since only one order allowed per side without compounding, target, stop and compound levels are shown sometimes even without trade being made. These can be considered as entry levels if there is no existing position. ShowPreviousLevels : Shows previous buy/sell target levels. When enabled, layout can look messy. StopMultiplyer: To Set trailing stop loss. BacktestYears: Number of years to include in backtest So far my test cases are: Positive : AAPL, AMZN, TSLA, RUN, VRT, ASX:APT Negative Test Cases: WPL, WHC, NHC, WOW, COL, NAB (All ASX stocks) Special test case: WDI Negative test cases still show losses in backtesting. I have attempted including many conditions to eliminate or reduce the loss. But, further efforts has resulted in reduction in profits in positive cases as well. Still experimenting. Will update whenever I find improvements. Comments and suggestions welcome :)
Estrategia Pine Script®
por Trendoscope
2