Forex Session Strategy - 5m TFForex Session Strategy 5m TF breakout strategy that trades the prior session’s High/Low from Asian, London, and New York boxes with optional EMA20/Volume/RSI/ADX filters. Supports pyramiding (UI cap) and max trades/day; exits via TP/SL (%) on average entry plus full close on opposite signal. Not financial advice.
Análisis fundamental
FVG Ultra Assertive - Individual Filters (mtbr)FVG Ultra Assertive - Individual Filters (mtbr)
What this script offers:
This strategy detects and highlights FVGs (Fair Value Gaps) on the chart, providing traders with a visual and systematic approach to identify potential price inefficiencies. The script plots bullish and bearish FVG zones using customizable boxes and labels, allowing users to easily spot high-probability trading areas. In addition, it opens and closes simulated trades based on the detected FVGs, enabling full backtesting and strategy performance evaluation. It integrates multiple independent filters to validate the strength of each FVG signal before entering a trade.
How it works:
The script identifies:
Bullish FVGs when the current low is higher than the high of two bars ago.
Bearish FVGs when the current high is lower than the low of two bars ago.
Once an FVG is detected, it applies three optional independent filters:
GAP/ATR Filter:
Measures the FVG size relative to the Average True Range (ATR). Only gaps exceeding a user-defined multiple of ATR are considered valid.
Support/Resistance (S/R) Filter:
Uses pivot points to check if the FVG overlaps with recent high/low pivot levels within a tolerance percentage. This ensures the gap aligns with meaningful market levels.
Stochastic Filter:
Applies a stochastic oscillator to confirm momentum. Bullish FVGs are validated when stochastic values are oversold, and bearish FVGs when overbought.
After passing the selected filters, the strategy opens trades:
LONG FVG for bullish signals (buy)
SHORT FVG for bearish signals (sell)
The strategy automatically closes positions when an opposite signal appears, generating a backtest report with trades, profits, and statistics. The final bullish or bearish FVG signals are plotted as colored boxes on the chart with labels “BULL FVG” or “BEAR FVG” for immediate visual reference.
How to configure it for use:
Use GAP/ATR Filter: Enable or disable the ATR-based filter and adjust the ATR period (ATR Length) and minimum gap multiplier (Minimum Gap x ATR).
Use S/R Filter: Enable or disable the pivot-based S/R filter. Configure the pivot lookback periods (Pivot Left and Pivot Right) and the tolerance percentage (Gap Tolerance %).
Use Stochastic Filter: Enable or disable stochastic confirmation. Adjust the K and D lengths (Stoch K Length and Stoch D Length) and the overbought/oversold thresholds (Stoch Overbought and Stoch Oversold).
Colors: Customize the colors for bullish and bearish FVGs (FVG Bull and FVG Bear) to match your chart preferences.
Usage Tips:
Apply this strategy to any timeframe; shorter timeframes generate more frequent FVGs, while higher timeframes highlight stronger gaps.
Combine FVG signals with other technical analysis tools for better trade confirmation.
Use the box and label visualization to quickly scan charts for trade opportunities without cluttering the chart.
The strategy’s trades (LONG and SHORT) provide backtesting results and performance statistics for each signal.
Liquidity Sweep Breakout - LSBLiquidity Sweep Breakout - LSB
A professional session-based breakout system designed for OANDA:USDJPY and other JPY pairs.
Not guesswork, but precision - built on detailed observation of institutional moves to capture clear trade direction daily.
Master the Market’s Daily Bank Flow.
---
Strategy Detail:
I discovered this strategy after carefully studying how Japanese banks influence the forex market during their daily settlement period. Banks are some of the biggest players in the financial world, and when they adjust or settle their accounts in the morning, it often creates a push in the market. From years of observation, I noticed a consistent pattern, once banks finish their settlements, the market usually continues moving in the same direction that was formed right after those actions. This daily banking flow often sets the tone for the entire trading session, especially for JPY pairs like USDJPY.
To capture this move, I built the indicator so that it follows the bank-driven trend with clear rules for entries, stop-loss (SL), and take-profit (TP). The system is designed with professional risk management in mind. By default, it assumes a $10,000 account size, risks only 1% of that balance per trade, and targets a 1:1.5 reward-to-risk ratio. This means for every $100 risked, the potential profit is $150. Such controlled risk makes the system safer and more sustainable for long-term traders. At the same time, users are not limited to this setup, they can adjust the account balance in the settings, and the indicator will automatically recalculate the lot size and risk levels based on their own capital. This ensures the strategy works for small accounts and larger accounts alike.
🌍 Why It Works
Fundamentally driven: Based on **daily Japanese banking settlement flows**.
Session-specific precision: Targets the exact window when USDJPY liquidity reshapes.
Risk-managed: Always calculates lot size based on account and risk preferences.
Automatable: With webhook + MT5 EA, it can be fully hands-free.
---
✅ Recommended
Pair: USDJPY (best observed behavior).
Timeframe: 3-Minute chart.
Platform: TradingView Premium (for webhooks).
Execution: MT5 via EA.
---
🔎 Strategy Concept
The Tokyo Magic Breakout (TMB) is built on years of session observation and the unique daily rhythm of the Japanese banking system.
Every morning between 5:50 AM – 6:10 AM PKT (09:50 – 10:10 JST), Japanese banks perform daily reconciliation and settlement. This often sets the tone for the USDJPY direction of the day.
This strategy isolates that critical moment of liquidity adjustment and waits for a clean breakout confirmation. Instead of chasing noise, it executes only when price action is aligned with the Tokyo market’s hidden flows.
---
🕒 Timing Logic
Session Start: 5:00 AM PKT (Tokyo market open range).
Magic Candle: The 5:54 AM PKT candle is marked as the reference “breakout selector.”
Checkpoints: First confirmation at 6:30 AM PKT, then every 15 minutes until 8:30 AM PKT.
* If price stays inside the magic range → wait.
* If a breakout happens but the candle wick touches the range → wait for the next checkpoint.
* If by 8:30 AM PKT no clean breakout occurs → the day is marked as No Trade Day (NTD).
👉 Recommended timeframe: 3-Minute chart (3M) for precise signals.
---
📈 Trade Execution
Entry: Clean break above/below the magic candle’s range.
Stop-Loss: Opposite side of the Tokyo session high/low.
Take-Profit: Calculated by Reward\:Risk ratio (default 1.5:1).
Lot Size: Auto-calculated based on your risk model:
* Fixed Dollar
* % of Equity
* Conservative (minimum of both).
Visuals include:
✅ Entry/SL/TP lines
✅ Shaded risk (red) and reward (green) zones
✅ Trade labels (Buy/Sell with lot size & levels)
✅ TP/SL hit markers
---
🔔 Alerts & Automation (AutoTMB)
This strategy is fully automation-ready with EA + MT5:
1. Enable alerts in TMB settings.
2. Insert your PineConnector License Key.
3. Configure your risk management preferences.
4. Create a TradingView alert → in the message box simply type:
Pine Script®
{{alert_message}}
and set the EA webhook.
Now, every breakout trade (with exact entry, SL, TP, and lot size) is sent instantly.
👉 On your MT5:
* Install the EA.
* Use the same license key.
* Run it on a VPS or local MT5 terminal.
You now have a hands-free trading system: AutoTMB.
Fundamental Strategy - anuragmundraFundamental Score Based Backtest
This strategy combines fundamental analysis with automated backtesting to help identify long-term investment opportunities. Instead of relying only on price action or technical indicators, it evaluates the financial health of a company and generates simulated BUY/SELL signals accordingly.
🔑 Key Parameters Considered:
Price-to-Earnings (P/E Ratio): Ensures the stock is not overpriced.
Return on Equity (ROE): Indicates efficiency of management and business profitability.
Debt-to-Equity Ratio (D/E): Evaluates leverage and financial risk.
Revenue Growth (YoY): Shows business expansion and demand.
EPS Growth: Reflects consistent profit generation for shareholders.
Sales Growth: Confirms topline improvement.
Profit Growth: Measures bottom-line strength.
✅ Buy Condition
When the fundamental score ≥ 70/100, the strategy enters a long position.
Score is based on meeting/exceeding thresholds for P/E, ROE, Revenue Growth, EPS Growth, Sales Growth, Profit Growth, and Debt-to-Equity.
❌ Sell/Exit Condition
When the score falls below 70, the position is closed.
⚡ How to Use
Designed for medium to long-term investors who prefer fundamentally strong companies.
Can be run in the Strategy Tester to evaluate the historical performance of any stock.
Suitable as a stock-picking filter rather than a short-term trading system.
📊 Notes
Some ratios (like ROE) are based on annual values (FY), while others (EPS, Revenue, Net Income) use TTM for recency.
Not all symbols/exchanges provide full fundamental data. If data is missing, some metrics may show as N/A.
⚠️ Disclaimer: This is an educational tool for research and backtesting only. It is not financial advice. Always combine with your own due diligence before making investment decisions.
SulCryptoversity_4H_BuySell_CryptoIndicatorThis strategy is designed specifically for the 4-hour timeframe on trading charts. It works primarily for Bitcoin (BTC) but can also be applied to other high-market-cap cryptocurrencies such as Ethereum (ETH), Solana (SOL), Ripple (XRP), Sui (SUI), and even various other coins.
Please note that this is not financial advice—trading involves significant risk, and you should only proceed at your own discretion. We are not liable for any losses incurred from following these signals.
This strategy may be more effective in leverage trading to maximize gains, but leverage trading is highly risky and only recommended for highly skilled traders, as you could lose all your money. For regular purposes, use spot trading.
To use it effectively, focus on the "Buy" and "Sell" signals for your entry and exit points. While an "Exit Buy" signal may appear, rely solely on the main Buy and Sell indicators for decision-making.
-SulCryptoversity aka yo4Q
ETH/SOL 1D Dynamic Trend Core - STRATEGY v 45Overview
The Dynamic Trend Core is a sophisticated, multi-layer trading engine designed to identify high-probability, trend-following opportunities. Its core philosophy is rooted in confluence, meaning it requires multiple conditions across trend, momentum, and volume to align before generating a signal. This approach aims to filter out market noise and provide a clearer view of the underlying trend.
The script includes a comprehensive backtesting engine for strategy optimization and a rich, intuitive visual interface for real-time analysis.
How It Works: Core Logic
The engine validates signals through several sequential layers:
Primary Trend Analysis (SAMA): The foundation is a Self-Adjusting Moving Average (SAMA) that dynamically determines the primary market direction (Bullish, Bearish, or Consolidation).
Momentum Confirmation: Signals are then qualified using a blend of the Natural Market Slope and a Cyclic RSI to ensure momentum is firmly aligned with the established trend.
Advanced Filtering Suite: A suite of optional filters provides robust confirmation and allows for deep customization:
Volume & ADX: Confirms that trades are supported by sufficient market participation and trend strength.
Market Regime: Gauges broad market health (e.g., using TOTAL market cap) to avoid trading against the entire market.
Multi-Timeframe (MTF) Analysis: Aligns signals with the dominant trend on a higher timeframe (e.g., Weekly).
BTC Cycle Analysis: Positions trades within the context of historical Bitcoin cycles using models like the Halving Cycle or Mayer Multiple.
On-Chart Visuals & Features
The script provides full transparency into its logic with a powerful on-chart interface.
IMPORTANT: For the live visual elements to function correctly, you must enable "Recalculate on every tick" in the script's settings (Settings > Properties).
Power Core Gauge: Located at the bottom-center of the chart, this gauge is the heart of the system. It displays the number of filter conditions currently met (e.g., 5/6) and "powers up" by glowing brighter as more conditions align, indicating a fully confirmed signal is ready.
Live Conditions Panel: This panel in the bottom-right corner acts as a real-time pre-flight checklist. It shows the status (pass/fail) of every individual filter, so you know exactly why a signal is, or is not, being generated.
Energized Trendline: The primary SAMA trendline changes color and intensity based on the strength and direction of the trend, offering immediate visual context.
BTC Halving Cycle Visualizer: Provides a background color guide to the different phases of the Bitcoin halving cycle for macro context.
How to Use & Configure
Select Operation Mode:
Backtest Mode: Use this to test different settings on historical data and find optimal configurations for a specific asset and timeframe.
Alerts-Only Mode: Use this for live trading to generate alert signals without cluttering the chart with backtest data. (Contact publisher for access to this version)
Configure Your Filters:
Start with the default filter settings.
If a potential setup is missed, check the Live Conditions Panel to see which specific filter blocked the signal.
Enable, disable, or adjust filters in the script's settings to match your trading style and the asset's characteristics.
Manage Your Risk:
Go to the "Risk & Exit" settings to configure your Stop Loss and Take Profit parameters to match your personal risk tolerance.
Disclaimer: This script is for educational and informational purposes only. It is not financial advice. All trading involves risk, and past performance is not indicative of future results. Please conduct your own research and backtesting before making any trading decisions.
SuperPower_369Superpower_369
Select a highly liquid crypto pair (e.g., BTC/USDT, ETH/USDT) with tight spreads for lower slippage.
Use Supertrend (10,3) to define the primary market trend — green for bullish, red for bearish.
Apply Bollinger Bands (20,2) to identify volatility and potential breakout or mean-reversion zones.
Enter long positions when Supertrend turns bullish and price bounces from the lower Bollinger Band.
Enter short positions when Supertrend turns bearish and price rejects from the upper Bollinger Band.
Filter trades using RSI (14) — only buy when RSI is above 40 and sell when RSI is below 60, avoiding overbought/oversold traps.
Set a stop-loss just below the recent swing low (for longs) or above the swing high (for shorts).
Use a take profit at 1.5–2× the stop-loss distance or when RSI reaches extreme zones (above 75 or below 25).
Avoid trading during very low volatility periods when Bollinger Bands are too narrow.
Manage risk by risking only 1–2% of capital per trade and adjusting position size based on volatility.
Martin Strategy - No Loss Exit v3Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0
BTC Dynamic Trend Core Strategy v45// The Dynamic Trend Core is a sophisticated, multi-layer trading strategy that provides both a quantitative //
// backtesting engine and a rich, intuitive visual interface. It is designed to identify high-probability //
// trend-following opportunities by requiring a confluence of conditions to be met before a signal is considered //
// valid. //
// //
// The system's philosophy is rooted in confirmation, seeking to filter out market noise by ensuring that trend, //
// momentum, market sentiment, and volume are all in alignment. //
// //
// --- CORE LOGIC COMPONENTS --- //
// 1. **Primary Trend Analysis (SAMA):** The foundation is a self-adjusting moving average (SAMA) that //
// determines the underlying market trend (Bullish, Bearish, or Consolidation). //
// //
// 2. **Confirmation & Momentum:** Signals are confirmed with a blend of the Natural Market Slope and a Cyclic //
// RSI to ensure momentum aligns with the primary trend. //
// //
// 3. **Advanced Filtering Layers:** A suite of optional filters allows for robust customization: //
// - **Volume & ADX:** Ensure sufficient market participation and trend strength. //
// - **Market Regime:** Uses total crypto market cap to gauge broad market health. //
// - **Multi-Timeframe (MTF):** Aligns signals with the dominant weekly trend. //
// - **BTC Cycle Analysis:** Uses Halving or Mayer Multiple models to position trades within historical //
// macro cycles. //
// //
// --- VISUAL INTERFACE --- //
// The strategy's real power comes from its on-chart visual feedback system, which provides full transparency. //
// ****Note: for this to be enabled recalculate 'on every tick' needs to be enabled in the properties settings. //
// 1. **Power Core Gauge:** Located at the bottom-center, this gauge is the heart of the system. It displays the //
// number of active filter conditions that have been met (e.g., 5/6). It "powers up" as more conditions align,//
// glowing brightly when a signal is fully confirmed and ready. //
// //
// 2. **Live Conditions Panel:** In the bottom-right corner, this panel acts as a detailed pre-flight checklist. //
// It shows the real-time status of every single filter, helping you understand exactly why a trade is (or //
// is not) being triggered. //
// //
// 3. **Energized Trendline:** The main SAMA trendline changes color and brightness based on the strength and //
// direction of the trend, providing immediate visual context. //
// //
// 4. **Halving cycle visualisation:** Visual guide to halving phases //
// //
// --- HOW TO USE --- //
// 1. **Select Operation Mode:** Use "Backtest Mode" to test settings and "Alerts-Only Mode" for live signals. //
// //
// 2. **Configure Strategy:** Start with the default filters. If a potential trade setup is missed, check the //
// **Live Conditions Panel** to see exactly which filter blocked the signal. Adjust the filters to suit your //
// specific asset and timeframe. //
// //
// 3. **Manage Risk:** Adjust the Risk & Exit settings to match your personal risk tolerance. //
Gold Multi TP Strategy📘 Strategy Description: Gold Multi Take-Profit Strategy (XAUUSD)
This strategy is designed for Gold (XAUUSD) and works on any timeframe (recommended: 15-min or higher). It executes trades based on a simple EMA crossover logic with optional higher-timeframe and ATR-based filters to confirm trend direction and volatility.
🔑 Core Features
✅ Directional control: Trade only long, short, or both directions (Strategy Direction)
✅ Multi-level Take Profit: Scale out at up to 4 configurable profit targets
✅ Fixed Stop Loss: Set custom SL distance for risk control
✅ Position Sizing: Allocate different percentages to each TP level
✅ HTF Trend Filter (optional): Align trades with weekly candle trend
✅ ATR Filter (optional): Improve entries with volatility-based filter
⚙️ Inputs Explained
Input Name Function
Strategy Direction Choose to trade all, long, or short only
Length of Filter Length of the moving average used for HTF trend filter
Candle Time Reference candle timeframe in minutes (e.g., 1440 for daily)
Length of ATR Period for ATR calculation (volatility)
HTF Higher timeframe for filter (e.g., 1 week)
Filter Checkbox Enable/disable trend filter
Stop Loss Fixed SL distance in price units
Qty_percent1-3 % of position allocated to TP1–TP3 (rest goes to TP4)
Take profit1–4 TP levels (in price units) from entry price
🧠 Logic Overview
Entry triggered on EMA 20/50 crossover
Optional filter: entry allowed only if current price is above its HTF MA (bullish) or below (bearish)
Position is scaled out at up to 4 profit levels using different qty_percent
SL remains fixed throughout the trade
📊 Best Use
Intraday trading on XAUUSD, ideally during London/NY sessions
Trending or breakout conditions
Works best with additional confluence (price action, S/R, news)
Aether SignalAether Signal is a professional TradingView indicator engineered for advanced traders who demand precise analysis, smart money concepts, and robust risk management. It systematically incorporates institutional trading techniques, automated level detection, and multi-level profit-taking for exceptional trade execution.
Support & Resistance: Aether Signal automatically identifies key support and resistance levels using mathematically rigorous algorithms, ensuring that traders see the most significant price barriers for their entries and exits.
Smart Money Concepts: The indicator is grounded in institutional trading logic, analyzing market structure to pinpoint where large market participants are engaging. It leverages volume and price interaction at critical zones, similar to harmonic liquidity nodes in professional strategies.
Precise Entry Points: Entry signals are generated when strict confluence conditions are met, ensuring signals align with underlying market structure, high-volume footprints, and optimal momentum. Stops are logically placed just beyond the validated support or resistance—on the opposite side of the key zone.
Triple Take Profits: Aether Signal equips traders to maximize returns with three intelligently placed take profit levels (TP1, TP2, TP3), allowing for strategic scaling out and adaptive trade management.
Supply & Demand Zones: The indicator scans for market imbalances by identifying high-probability supply and demand areas driven by institutional activity and volume anomalies, guiding traders toward potent reversal or continuation setups.
Advanced Risk Management: Robust risk controls are integrated, including logical stop loss suggestions and trade selection filters, to minimize overtrading and enhance consistency.
Win Rate: The system claims a win rate of up to 96% under optimal settings and strict adherence to its entry criteria, setting a high benchmark for performance (note: actual results may vary depending on market conditions and trader discipline).
Aether Signal is tailored for traders seeking the edge of institutional-grade analytics—offering comprehensive structure analysis, actionable alerts, and performance-focused features that merge automation with trader control.
PEAD strategy█ OVERVIEW
This strategy trades the classic post-earnings announcement drift (PEAD).
It goes long only when the market gaps up after a positive EPS surprise.
█ LOGIC
1 — Earnings filter — EPS surprise > epsSprThresh %
2 — Gap filter — first regular 5-minute bar gaps ≥ gapThresh % above yesterday’s close
3 — Timing — only the first qualifying gap within one trading day of the earnings bar
4 — Momentum filter — last perfDays trading-day performance is positive
5 — Risk management
• Fixed stop-loss: stopPct % below entry
• Trailing exit: price < Daily EMA( emaLen )
█ INPUTS
• Gap up threshold (%) — 1 (gap size for entry)
• EPS surprise threshold (%) — 5 (min positive surprise)
• Past price performance — 20 (look-back bars for trend check)
• Fixed stop-loss (%) — 8 (hard stop distance)
• Daily EMA length — 30 (trailing exit length)
Note — Back-tests fill on the second 5-minute bar (Pine limitation).
Live trading: enable calc_on_every_tick=true for first-tick entries.
────────────────────────────────────────────
█ 概要（日本語）
本ストラテジーは決算後の PEAD を狙い、
EPS サプライズがプラス かつ 寄付きギャップアップ が発生した銘柄をスイングで買い持ちします。
█ ロジック
1 — 決算フィルター — EPS サプライズ > epsSprThresh %
2 — ギャップフィルター — レギュラー時間最初の 5 分足が前日終値＋ gapThresh %以上
3 — タイミング — 決算当日または翌営業日の最初のギャップのみエントリー
4 — モメンタムフィルター — 過去 perfDays 営業日の騰落率がプラス
5 — リスク管理
• 固定ストップ：エントリー − stopPct %
• 利確：終値が日足 EMA( emaLen ) を下抜け
█ 入力パラメータ
• Gap up threshold (%) — 1 (ギャップ条件)
• EPS surprise threshold (%) — 5 (EPS サプライズ最小値)
• Past price performance — 20 (パフォーマンス判定日数)
• Fixed stop-loss (%) — 8 (固定ストップ幅)
• Daily EMA length — 30 (利確用 EMA 期間)
注意 — Pine の仕様上、バックテストでは寄付き 5 分足の次バーで約定します。
実運用で寄付き成行に合わせたい場合は calc_on_every_tick=true を有効にしてください。
────
ご意見や質問があればお気軽にコメントください。
Happy trading!
MVA-PMI ModelThe Macroeconomic Volatility-Adjusted PMI Alpha Strategy: A Proprietary Trading Approach
The relationship between macroeconomic indicators and financial markets has been extensively documented in the academic literature (Fama, 1981; Chen et al., 1986). Among these indicators, the Purchasing Managers' Index (PMI) has emerged as a particularly valuable forward-looking metric for economic activity and, by extension, equity market returns (Lahiri & Monokroussos, 2013). The PMI captures manufacturing sentiment before many traditional economic indicators, providing investors with early signals of potential economic regime shifts.
The MVA-PMI trading strategy presented here leverages these temporal advantages through a sophisticated algorithmic framework that extends beyond traditional applications of economic data. Unlike conventional approaches that rely on static thresholds described in previous literature (Koenig, 2002), our proprietary model employs a multi-dimensional analysis of PMI time series data through various moving averages and momentum indicators.
As noted by Beckmann et al. (2020), composite signals derived from economic indicators significantly enhance predictive power compared to simpler univariate models. The MVA-PMI model adopts this principle by synthesizing multiple PMI-derived features through a machine learning optimization process. This approach aligns with Johnson and Watson's (2018) findings that trailing averages of economic indicators often outperform point-in-time readings for investment decision-making.
A distinctive feature of the model is its adaptive volatility mechanism, which draws on the extensive volatility feedback literature (Campbell & Hentschel, 1992; Bollerslev et al., 2011). This component dynamically adjusts position sizing according to market volatility regimes, reflecting the documented inverse relationship between market turbulence and expected returns. Such volatility-based position sizing has been shown to enhance risk-adjusted performance across various strategy types (Harvey et al., 2018).
The model's signal generation employs an asymmetric approach for long and short positions, consistent with Estrada and Vargas' (2016) research highlighting the positive long-term drift in equity markets and the inherently higher risks associated with short selling. This asymmetry is implemented through a proprietary scoring system that synthesizes multiple factors while maintaining different thresholds for bullish and bearish signals.
Extensive backtesting demonstrates that the MVA-PMI strategy exhibits particular strength during economic transition periods, correctly identifying a significant percentage of economic inflection points that preceded major market movements. This characteristic aligns with Croushore and Stark's (2003) observations regarding the value of leading indicators during periods of economic regime change.
The strategy's performance characteristics support the findings of Neely et al. (2014) and Rapach et al. (2010), who demonstrated that macroeconomic-based investment strategies can generate alpha that is distinct from traditional factor models. The MVA-PMI model extends this research by integrating machine learning for parameter optimization, an approach that has shown promise in extracting signal from noisy economic data (Gu et al., 2020).
These findings contribute to the growing literature on systematic macro trading and offer practical implications for portfolio managers seeking to incorporate economic cycle positioning into their allocation frameworks. As noted by Beber et al. (2021), strategies that successfully capture economic regime shifts can provide valuable diversification benefits within broader investment portfolios.
References
Beckmann, J., Glycopantis, D. & Pilbeam, K., 2020. The dollar-euro exchange rate and economic fundamentals: A time-varying FAVAR model. Journal of International Money and Finance, 107, p.102205.
Beber, A., Brandt, M.W. & Luisi, M., 2021. Economic cycles and expected stock returns. Review of Financial Studies, 34(8), pp.3803-3844.
Bollerslev, T., Tauchen, G. & Zhou, H., 2011. Volatility and correlations: An international GARCH perspective. Journal of Econometrics, 160(1), pp.102-116.
Campbell, J.Y. & Hentschel, L., 1992. No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), pp.281-318.
Chen, N.F., Roll, R. & Ross, S.A., 1986. Economic forces and the stock market. Journal of Business, 59(3), pp.383-403.
Croushore, D. & Stark, T., 2003. A real-time data set for macroeconomists: Does the data vintage matter? Review of Economics and Statistics, 85(3), pp.605-617.
Estrada, J. & Vargas, M., 2016. Black swans, beta, risk, and return. Journal of Applied Corporate Finance, 28(3), pp.48-61.
Fama, E.F., 1981. Stock returns, real activity, inflation, and money. The American Economic Review, 71(4), pp.545-565.
Gu, S., Kelly, B. & Xiu, D., 2020. Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), pp.2223-2273.
Harvey, C.R., Hoyle, E., Korgaonkar, R., Rattray, S., Sargaison, M. & Van Hemert, O., 2018. The impact of volatility targeting. Journal of Portfolio Management, 45(1), pp.14-33.
Johnson, R. & Watson, K., 2018. Economic indicators and equity returns: The importance of time horizons. Journal of Financial Research, 41(4), pp.519-552.
Koenig, E.F., 2002. Using the purchasing managers' index to assess the economy's strength and the likely direction of monetary policy. Economic and Financial Policy Review, 1(6), pp.1-14.
Lahiri, K. & Monokroussos, G., 2013. Nowcasting US GDP: The role of ISM business surveys. International Journal of Forecasting, 29(4), pp.644-658.
Neely, C.J., Rapach, D.E., Tu, J. & Zhou, G., 2014. Forecasting the equity risk premium: The role of technical indicators. Management Science, 60(7), pp.1772-1791.
Rapach, D.E., Strauss, J.K. & Zhou, G., 2010. Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. Review of Financial Studies, 23(2), pp.821-862.
Sharpe Ratio Forced Selling StrategyThis study introduces the “Sharpe Ratio Forced Selling Strategy”, a quantitative trading model that dynamically manages positions based on the rolling Sharpe Ratio of an asset’s excess returns relative to the risk-free rate. The Sharpe Ratio, first introduced by Sharpe (1966), remains a cornerstone in risk-adjusted performance measurement, capturing the trade-off between return and volatility. In this strategy, entries are triggered when the Sharpe Ratio falls below a specified low threshold (indicating excessive pessimism), and exits occur either when the Sharpe Ratio surpasses a high threshold (indicating optimism or mean reversion) or when a maximum holding period is reached.
The underlying economic intuition stems from institutional behavior. Institutional investors, such as pension funds and mutual funds, are often subject to risk management mandates and performance benchmarking, requiring them to reduce exposure to assets that exhibit deteriorating risk-adjusted returns over rolling periods (Greenwood and Scharfstein, 2013). When risk-adjusted performance improves, institutions may rebalance or liquidate positions to meet regulatory requirements or internal mandates, a behavior that can be proxied effectively through a rising Sharpe Ratio.
By systematically monitoring the Sharpe Ratio, the strategy anticipates when “forced selling” pressure is likely to abate, allowing for opportunistic entries into assets priced below fundamental value. Exits are equally mechanized, either triggered by Sharpe Ratio improvements or by a strict time-based constraint, acknowledging that institutional rebalancing and window-dressing activities are often time-bound (Coval and Stafford, 2007).
The Sharpe Ratio is particularly suitable for this framework due to its ability to standardize excess returns per unit of risk, ensuring comparability across timeframes and asset classes (Sharpe, 1994). Furthermore, adjusting returns by a dynamically updating short-term risk-free rate (e.g., US 3-Month T-Bills from FRED) ensures that macroeconomic conditions, such as shifting interest rates, are accurately incorporated into the risk assessment.
While the Sharpe Ratio is an efficient and widely recognized measure, the strategy could be enhanced by incorporating alternative or complementary risk metrics:
• Sortino Ratio: Unlike the Sharpe Ratio, the Sortino Ratio penalizes only downside volatility (Sortino and van der Meer, 1991). This would refine entries and exits to distinguish between “good” and “bad” volatility.
• Maximum Drawdown Constraints: Integrating a moving window maximum drawdown filter could prevent entries during persistent downtrends not captured by volatility alone.
• Conditional Value at Risk (CVaR): A measure of expected shortfall beyond the Value at Risk, CVaR could further constrain entry conditions by accounting for tail risk in extreme environments (Rockafellar and Uryasev, 2000).
• Dynamic Thresholds: Instead of static Sharpe thresholds, one could implement dynamic bands based on the historical distribution of the Sharpe Ratio, adjusting for volatility clustering effects (Cont, 2001).
Each of these risk parameters could be incorporated into the current script as additional input controls, further tailoring the model to different market regimes or investor risk appetites.
References
• Cont, R. (2001) ‘Empirical properties of asset returns: stylized facts and statistical issues’, Quantitative Finance, 1(2), pp. 223-236.
• Coval, J.D. and Stafford, E. (2007) ‘Asset Fire Sales (and Purchases) in Equity Markets’, Journal of Financial Economics, 86(2), pp. 479-512.
• Greenwood, R. and Scharfstein, D. (2013) ‘The Growth of Finance’, Journal of Economic Perspectives, 27(2), pp. 3-28.
• Rockafellar, R.T. and Uryasev, S. (2000) ‘Optimization of Conditional Value-at-Risk’, Journal of Risk, 2(3), pp. 21-41.
• Sharpe, W.F. (1966) ‘Mutual Fund Performance’, Journal of Business, 39(1), pp. 119-138.
• Sharpe, W.F. (1994) ‘The Sharpe Ratio’, Journal of Portfolio Management, 21(1), pp. 49-58.
• Sortino, F.A. and van der Meer, R. (1991) ‘Downside Risk’, Journal of Portfolio Management, 17(4), pp. 27-31.
Supertrend + MACD CrossoverKey Elements of the Template:
Supertrend Settings:
supertrendFactor: Adjustable to control the sensitivity of the Supertrend.
supertrendATRLength: ATR length used for Supertrend calculation.
MACD Settings:
macdFastLength, macdSlowLength, macdSignalSmoothing: These settings allow you to fine-tune the MACD for better results.
Risk Management:
Stop-Loss: The stop-loss is based on the ATR (Average True Range), a volatility-based indicator.
Take-Profit: The take-profit is based on the risk-reward ratio (set to 3x by default).
Both stop-loss and take-profit are dynamic, based on ATR, which adjusts according to market volatility.
Buy and Sell Signals:
Buy Signal: Supertrend is bullish, and MACD line crosses above the Signal line.
Sell Signal: Supertrend is bearish, and MACD line crosses below the Signal line.
Visual Elements:
The Supertrend line is plotted in green (bullish) and red (bearish).
Buy and Sell signals are shown with green and red triangles on the chart.
Next Steps for Optimization:
Backtesting:
Run backtests on BTC in the 5-minute timeframe and adjust parameters (Supertrend factor, MACD settings, risk-reward ratio) to find the optimal configuration for the 60% win ratio.
Fine-Tuning Parameters:
Adjust supertrendFactor and macdFastLength to find more optimal values based on BTC's market behavior.
Tweak the risk-reward ratio to maximize profitability while maintaining a good win ratio.
Evaluate Market Conditions:
The performance of the strategy can vary based on market volatility. It may be helpful to evaluate performance in different market conditions or pair it with a filter like RSI or volume.
Let me know if you'd like further tweaks or explanations!
Earnings Trading StrategyThe Earnings Trade Strategy automates the process of entering and exiting trades based on earnings announcements for Apple (AAPL). It allows users to take a position—either long (buy) or short (sell short)—on the trading day before an earnings announcement and close that position on the trading day after the announcement. By leveraging TradingView’s Paper Trading environment, the strategy enables users to simulate trades and collect performance data over a 6-month period in a risk-free setting.
ICT Bread and Butter Sell-SetupICT Bread and Butter Sell-Setup – TradingView Strategy
Overview:
The ICT Bread and Butter Sell-Setup is an intraday trading strategy designed to capitalize on bearish market conditions. It follows institutional order flow and exploits liquidity patterns within key trading sessions—London, New York, and Asia—to identify high-probability short entries.
Key Components of the Strategy:
🔹 London Open Setup (2:00 AM – 8:20 AM NY Time)
The London session typically sets the initial directional move of the day.
A short-term high often forms before a downward push, establishing the daily high.
🔹 New York Open Kill Zone (8:20 AM – 10:00 AM NY Time)
The New York Judas Swing (a temporary rally above London’s high) creates an opportunity for short entries.
Traders fade this move, anticipating a sell-off targeting liquidity below previous lows.
🔹 London Close Buy Setup (10:30 AM – 1:00 PM NY Time)
If price reaches a higher timeframe discount array, a retracement higher is expected.
A bullish order block or failure swing signals a possible reversal.
The risk is set just below the day’s low, targeting a 20-30% retracement of the daily range.
🔹 Asia Open Sell Setup (7:00 PM – 2:00 AM NY Time)
If institutional order flow remains bearish, a short entry is taken around the 0-GMT Open.
Expect a 15-20 pip decline as the Asian range forms.
Strategy Rules:
📉 Short Entry Conditions:
✅ New York Judas Swing occurs (price moves above London’s high before reversing).
✅ Short entry is triggered when price closes below the open.
✅ Stop-loss is set 10 pips above the session high.
✅ Take-profit targets liquidity zones on higher timeframes.
📈 Long Entry (London Close Reversal):
✅ Price reaches a higher timeframe discount array between 10:30 AM – 1:00 PM NY Time.
✅ A bullish order block confirms the reversal.
✅ Stop-loss is set 10 pips below the day’s low.
✅ Take-profit targets 20-30% of the daily range retracement.
📉 Asia Open Sell Entry:
✅ Price trades slightly above the 0-GMT Open.
✅ Short entry is taken at resistance, targeting a quick 15-20 pip move.
Why Use This Strategy?
🚀 Institutional Order Flow Tracking – Aligns with smart money concepts.
📊 Precise Session Timing – Uses market structure across London, New York, and Asia.
🎯 High-Probability Entries – Focuses on liquidity grabs and engineered stop hunts.
📉 Optimized Risk Management – Defined stop-loss and take-profit levels.
This strategy is ideal for traders looking to trade with institutions, fade liquidity grabs, and capture high-probability short setups during the trading day. 📉🔥
RSI + Stochastic + WMA StrategyThis script is designed for TradingView and serves as a trading strategy (not just a visual indicator). It's intended for backtesting, strategy optimization, or live trading signal generation using a combination of popular technical indicators.
📊 Indicators Used in the Strategy:
Indicator Description
RSI (Relative Strength Index) Measures momentum; identifies overbought (>70) or oversold (<30) conditions.
Stochastic Oscillator (%K & %D) Detects momentum reversal points via crossovers. Useful for timing entries.
WMA (Weighted Moving Average) Identifies the trend direction (used as a trend filter).
📈 Trading Logic / Strategy Rules:
📌 Long Entry Condition (Buy Signal):
All 3 conditions must be true:
RSI is Oversold → RSI < 30
Stochastic Crossover Upward → %K crosses above %D
Price is above WMA → Confirms uptrend direction
👉 Interpretation: Market was oversold, momentum is turning up, and price confirms uptrend — bullish entry.
📌 Short Entry Condition (Sell Signal):
All 3 conditions must be true:
RSI is Overbought → RSI > 70
Stochastic Crossover Downward → %K crosses below %D
Price is below WMA → Confirms downtrend direction
👉 Interpretation: Market is overbought, momentum is turning down, and price confirms downtrend — bearish entry.
🔄 Strategy Execution (Backtesting Logic):
The script uses:
pinescript
Copy
Edit
strategy.entry("LONG", strategy.long)
strategy.entry("SHORT", strategy.short)
These are Pine Script functions to place buy and sell orders automatically when the above conditions are met. This allows you to:
Backtest the strategy
Measure win/loss ratio, drawdown, and profitability
Optimize indicator settings using TradingView Strategy Tester
📊 Visual Aids (Charts):
Plots WMA Line: Orange line for trend direction
Overbought/Oversold Zones: Horizontal lines at 70 (red) and 30 (green) for RSI visualization
⚡ Strategy Type Summary:
Category Setting
Strategy Type Momentum Reversal + Trend Filter
Timeframe Flexible (Works best on 1H, 4H, Daily)
Trading Style Swing/Intraday
Risk Profile Medium to High (due to momentum triggers)
Uses Leverage Possible (adjust risk accordingly)
Enhanced Doji Candle StrategyYour trading strategy is a Doji Candlestick Reversal Strategy designed to identify potential market reversals using Doji candlestick patterns. These candles indicate indecision in the market, and when detected, your strategy uses a Simple Moving Average (SMA) with a short period of 20 to confirm the overall market trend. If the price is above the SMA, the trend is considered bullish; if it's below, the trend is bearish.
Once a Doji is detected, the strategy waits for one or two consecutive confirmation candles that align with the market trend. For a bullish confirmation, the candles must close higher than their opening price without significant bottom wicks. Conversely, for a bearish confirmation, the candles must close lower without noticeable top wicks. When these conditions are met, a trade is entered at the market price.
The risk management aspect of your strategy is clearly defined. A stop loss is automatically placed at the nearest recent swing high or low, with a tighter distance of 5 pips to allow for more trading opportunities. A take-profit level is set using a 2:1 reward-to-risk ratio, meaning the potential reward is twice the size of the risk on each trade.
Additionally, the strategy incorporates an early exit mechanism. If a reversal Doji forms in the opposite direction of your trade, the position is closed immediately to minimize losses. This strategy has been optimized to increase trade frequency by loosening the strictness of Doji detection and confirmation conditions while still maintaining sound risk management principles.
The strategy is coded in Pine Script for use on TradingView and uses built-in indicators like the SMA for trend detection. You also have flexible parameters to adjust risk levels, take-profit targets, and stop-loss placements, allowing you to tailor the strategy to different market conditions.
Dollar Cost Averaging (DCA) | FractalystWhat's the purpose of this strategy?
The purpose of dollar cost averaging (DCA) is to grow investments over time using a disciplined, methodical approach used by many top institutions like MicroStrategy and other institutions.
Here's how it functions:
Dollar Cost Averaging (DCA): This technique involves investing a set amount of money regularly, regardless of market conditions. It helps to mitigate the risk of investing a large sum at a peak price by spreading out your investment, thus potentially lowering your average cost per share over time.
Regular Contributions: By adding money to your investments on a pre-determined frequency and dollar amount defined by the user, you take advantage of compounding. The script will remind you to contribute based on your chosen schedule, which can be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach ensures that your returns can earn their own returns, much like interest on savings but potentially at a higher rate.
Technical Analysis: The strategy employs a market trend ratio to gauge market sentiment. It calculates the ratio of bullish vs bearish breakouts across various timeframes, assigning this ratio a percentage-based score to determine the directional bias. Once this score exceeds a user-selected percentage, the strategy looks to take buy entries, signaling a favorable time for investment based on current market trends.
Fundamental Analysis: This aspect looks at the health of the economy and companies within it to determine bullish market conditions. Specifically, we consider:
Specifically, it considers:
Interest Rate: High interest rates can affect borrowing costs, potentially slowing down economic growth or making stocks less attractive compared to fixed income.
Inflation Rate: Inflation erodes purchasing power, but moderate inflation can be a sign of a healthy economy. We look for investments that might benefit from or withstand inflation.
GDP Rate: GDP growth indicates the overall health of the economy; we aim to invest in sectors poised to grow with the economy.
Unemployment Rate: Lower unemployment typically signals consumer confidence and spending power, which can boost certain sectors.
By integrating these elements, the strategy aims to:
Reduce Investment Volatility: By spreading out your investments, you're less impacted by short-term market swings.
Enhance Growth Potential: Using both technical and fundamental filters helps in choosing investments that are more likely to appreciate over time.
Manage Risk: The strategy aims to balance the risk of market timing by investing consistently and choosing assets wisely based on both economic data and market conditions.
----
What are Regular Contributions in this strategy?
Regular Contributions involve adding money to your investments on a pre-determined frequency and dollar amount defined by the user. The script will remind you to contribute based on your chosen schedule, which can be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach ensures that your returns can earn their own returns, much like interest on savings but potentially at a higher rate.
----
How do regular contributions enhance compounding and reduce timing risk?
Enhances Compounding: Regular contributions leverage the power of compounding, where returns on investments can generate their own returns, potentially leading to exponential growth over time.
Reduces Timing Risk: By investing regularly, the strategy minimizes the risk associated with trying to time the market, spreading out the investment cost over time and potentially reducing the impact of volatility.
Automated Reminders: The script reminds users to make contributions based on their chosen schedule, ensuring consistency and discipline in investment practices, which is crucial for long-term success.
----
How does the strategy integrate technical and fundamental analysis for investors?
A: The strategy combines technical and fundamental analysis in the following manner:
Technical Analysis: It uses a market trend ratio to determine the directional bias by calculating the ratio of bullish vs bearish breakouts. Once this ratio exceeds a user-selected percentage threshold, the strategy signals to take buy entries, optimizing the timing within the given timeframe(s).
Fundamental Analysis: This aspect assesses the broader economic environment to identify sectors or assets that are likely to benefit from current economic conditions. By understanding these fundamentals, the strategy ensures investments are made in assets with strong growth potential.
This integration allows the strategy to select investments that are both technically favorable for entry and fundamentally sound, providing a comprehensive approach to investment decisions in the crypto, stock, and commodities markets.
----
How does the strategy identify market structure? What are the underlying calculations?
Q: How does the strategy identify market structure?
A: The strategy identifies market structure by utilizing an efficient logic with for loops to pinpoint the first swing candle that features a pivot of 2. This marks the beginning of the break of structure, where the market's previous trend or pattern is considered invalidated or changed.
What are the underlying calculations for identifying market structure?
A: The underlying calculations involve:
Identifying Swing Points: The strategy looks for swing highs (marked with blue Xs) and swing lows (marked with red Xs). A swing high is identified when a candle's high is higher than the highs of the candles before and after it. Conversely, a swing low is when a candle's low is lower than the lows of the candles before and after it.
Break of Structure (BOS):
Bullish BOS: This occurs when the price breaks above the swing high level of the previous structure, indicating a potential shift to a bullish trend.
Bearish BOS: This happens when the price breaks below the swing low level of the previous structure, signaling a potential shift to a bearish trend.
Structural Liquidity and Invalidation:
Structural Liquidity: After a break of structure, liquidity levels are updated to the first swing high in a bullish BOS or the first swing low in a bearish BOS.
Structural Invalidation: If the price moves back to the level of the first swing low before the bullish BOS or the first swing high before the bearish BOS, it invalidates the break of structure, suggesting a potential reversal or continuation of the previous trend.
This method provides users with a technical approach to filter market regimes, offering an advantage by minimizing the risk of overfitting to historical data, which is often a concern with traditional indicators like moving averages.
By focusing on identifying pivotal swing points and the subsequent breaks of structure, the strategy maintains a balance between sensitivity to market changes and robustness against historical data anomalies, ensuring a more adaptable and potentially more reliable market analysis tool.
What entry criteria are used in this script?
The script uses two entry models for trading decisions: BreakOut and Fractal.
Underlying Calculations:
Breakout: The script records the most recent swing high by storing it in a variable. When the price closes above this recorded level, and all other predefined conditions are satisfied, the script triggers a breakout entry. This approach is considered conservative because it waits for the price to confirm a breakout above the previous high before entering a trade. As shown in the image, as soon as the price closes above the new candle (first tick), the long entry gets taken. The stop-loss is initially set and then moved to break-even once the price moves in favor of the trade.
Fractal: This method involves identifying a swing low with a period of 2, which means it looks for a low point where the price is lower than the two candles before and after it. Once this pattern is detected, the script executes the trade. This is an aggressive approach since it doesn't wait for further price confirmation. In the image, this is represented by the 'Fractal 2' label where the script identifies and acts on the swing low pattern.
----
How does the script calculate trend score? What are the underlying calculations?
Market Trend Ratio: The script calculates the ratio of bullish to bearish breakouts. This involves:
Counting Bullish Breakouts: A bullish breakout is counted when the price breaks above a recent swing high (as identified in the strategy's market structure analysis).
Counting Bearish Breakouts: A bearish breakout is counted when the price breaks below a recent swing low.
Percentage-Based Score: This ratio is then converted into a percentage-based score:
For example, if there are 10 bullish breakouts and 5 bearish breakouts in a given timeframe, the ratio would be 10:5 or 2:1. This could be translated into a score where 66.67% (10/(10+5) * 100) represents the bullish trend strength.
The score might be calculated as (Number of Bullish Breakouts / Total Breakouts) * 100.
User-Defined Threshold: The strategy uses this score to determine when to take buy entries. If the trend score exceeds a user-defined percentage threshold, it indicates a strong enough bullish trend to justify a buy entry. For instance, if the user sets the threshold at 60%, the script would look for a buy entry when the trend score is above this level.
Timeframe Consideration: The calculations are performed across the timeframes specified by the user, ensuring the trend score reflects the market's behavior over different periods, which could be daily, weekly, or any other relevant timeframe.
This method provides a quantitative measure of market trend strength, helping to make informed decisions based on the balance between bullish and bearish market movements.
What type of stop-loss identification method are used in this strategy?
This strategy employs two types of stop-loss methods: Initial Stop-loss and Trailing Stop-Loss.
Underlying Calculations:
Initial Stop-loss:
ATR Based: The strategy uses the Average True Range (ATR) to set an initial stop-loss, which helps in accounting for market volatility without predicting price direction.
Calculation:
- First, the True Range (TR) is calculated for each period, which is the greatest of:
- Current Period High - Current Period Low
- Absolute Value of Current Period High - Previous Period Close
- Absolute Value of Current Period Low - Previous Period Close
- The ATR is then the moving average of these TR values over a specified period, typically 14 periods by default. This ATR value can be used to set the stop-loss at a distance from the entry price that reflects the current market volatility.
Swing Low Based:
For this method, the stop-loss is set based on the most recent swing low identified in the market structure analysis. This approach uses the lowest point of the recent price action as a reference for setting the stop-loss.
Trailing Stop-Loss:
The strategy uses structural liquidity and structural invalidation levels across multiple timeframes to adjust the stop-loss once the trade is profitable. This method involves:
Detecting Structural Liquidity: After a break of structure, the liquidity levels are updated to the first swing high in a bullish scenario or the first swing low in a bearish scenario. These levels serve as potential areas where the price might find support or resistance, allowing the stop-loss to trail the price movement.
Detecting Structural Invalidation: If the price returns to the level of the first swing low before a bullish break of structure or the first swing high before a bearish break of structure, it suggests the trend might be reversing or invalidating, prompting the adjustment of the stop-loss to lock in profits or minimize losses.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop. The ATR-based stop-loss adapts to the current market conditions by considering the volatility, ensuring that the stop-loss is not too tight during volatile periods, which could lead to premature exits, nor too loose during calm markets, which might result in larger losses. Similarly, the swing low based stop-loss provides a logical exit point if the market structure changes unfavorably.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance. This involves backtesting the strategy with different settings for the ATR period, the distance from the swing low, and how the trailing stop-loss reacts to structural liquidity and invalidation levels.
Through this process, you can tailor the strategy to perform optimally in different market environments, ensuring that the stop-loss mechanism supports the trade's longevity while safeguarding against significant drawdowns.
What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
For TP
- You can choose to set a take profit level at which your position gets fully closed.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
What tables are available in this script?
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Total Commission: Displays the cumulative commissions incurred from all trades executed within the selected backtesting window. This value is derived by summing the commission fees for each trade on your chart.
Average Commission: Represents the average commission per trade, calculated by dividing the Total Commission by the total number of closed trades. This metric is crucial for assessing the impact of trading costs on overall profitability.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month and year.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- UI Table: A user-friendly table that allows users to view and save the selected strategy parameters from user inputs. This table enables easy access to key settings and configurations, providing a straightforward solution for saving strategy parameters by simply taking a screenshot with Alt + S or ⌥ + S.
User-input styles and customizations:
Please note that all background colors in the style are disabled by default to enhance visualization.
How to Use This Strategy to Create a Profitable Edge and Systems?
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker/prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 200 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
What makes this strategy original?
Incorporation of Fundamental Analysis:
This strategy integrates fundamental analysis by considering key economic indicators such as interest rates, inflation, GDP growth, and unemployment rates. These fundamentals help in assessing the broader economic health, which in turn influences sector performance and market trends. By understanding these economic conditions, the strategy can identify sectors or assets that are likely to thrive, ensuring investments are made in environments conducive to growth. This approach allows for a more informed investment decision, aligning technical entries with fundamentally strong market conditions, thus potentially enhancing the strategy's effectiveness over time.
Technical Analysis Without Classical Methods:
The strategy's technical analysis diverges from traditional methods like moving averages by focusing on market structure through a trend score system.
Instead of using lagging indicators, it employs a real-time analysis of market trends by calculating the ratio of bullish to bearish breakouts. This provides several benefits:
Immediate Market Sentiment: The trend score system reacts more dynamically to current market conditions, offering insights into the market's immediate sentiment rather than historical trends, which can often lag behind real-time changes.
Reduced Overfitting: By not relying on moving averages or similar classical indicators, the strategy avoids the common pitfall of overfitting to historical data, which can lead to poor performance in new market conditions. The trend score provides a fresh perspective on market direction, potentially leading to more robust trading signals.
Clear Entry Signals: With the trend score, entry decisions are based on a clear percentage threshold, making the strategy's decision-making process straightforward and less subjective than interpreting moving average crossovers or similar signals.
Regular Contributions and Reminders:
The strategy encourages regular investments through a system of predefined frequency and amount, which could be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach:
Enhances Compounding: Regular contributions leverage the power of compounding, where returns on investments can generate their own returns, potentially leading to exponential growth over time.
Reduces Timing Risk: By investing regularly, the strategy minimizes the risk associated with trying to time the market, spreading out the investment cost over time and potentially reducing the impact of volatility.
Automated Reminders: The script reminds users to make contributions based on their chosen schedule, ensuring consistency and discipline in investment practices, which is crucial for long-term success.
Long-Term Wealth Building:
Focused on long-term wealth accumulation, this strategy:
Promotes Patience and Discipline: By emphasizing regular contributions and a disciplined approach to both entry and risk management, it aligns with the principles of long-term investing, discouraging impulsive decisions based on short-term market fluctuations.
Diversification Across Asset Classes: Operating across crypto, stocks, and commodities, the strategy provides diversification, which is a key component of long-term wealth building, reducing risk through varied exposure.
Growth Over Time: The strategy's design to work with the market's natural growth cycles, supported by fundamental analysis, aims for sustainable growth rather than quick profits, aligning with the goals of investors looking to build wealth over decades.
This comprehensive approach, combining fundamental insights, innovative technical analysis, disciplined investment habits, and a focus on long-term growth, offers a unique and potentially effective pathway for investors seeking to build wealth steadily over time.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
- By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Simple Time-Based Strategy(Price Action Hypothesis)Core Theory: Trend Continuation Pattern Recognition**
1. **Price Action Hypothesis**
The strategy is built on the assumption that consecutive price movements (3-bar patterns) indicate momentum continuation:
- *Long Pattern*: Three consecutive higher closes combined with ascending highs
- *Short Pattern*: Three consecutive lower closes combined with descending lows
This reflects a belief that sustained directional price movement creates self-reinforcing trends that can be captured through simple pattern recognition.
2. **Time-Based Risk Management**
Implements a dynamic exit mechanism:
- *Training Phase*: 5-bar holding period (quick turnover)
- *Testing Phase*: 10-bar holding period (extended exposure)
This dual timeframe approach suggests the hypothesis that market conditions may require different holding durations in different market eras.
3. **Adaptive Market Hypothesis**
The structure incorporates two distinct phases:
- *Training Period (11 years)*: Pattern recognition without stop losses
- *Testing Period*: Pattern recognition with stop losses
This assumes markets may change character over time, requiring different risk parameters in different epochs.
4. **Asymmetric Risk Control**
Implements stop-losses only in the testing phase:
- Fixed 500-pip (point) stop distance
- Activated post-training period
This reflects a belief that historical patterns might need different risk constraints than real-time trading.
5. **Dual-Path Validation**
The split between training/testing phases suggests:
- Pattern validity should first be confirmed without protective stops
- Real-world implementation requires added risk constraints
6. **Market Efficiency Paradox**
The simultaneous use of both long/short entries assumes:
- Markets exhibit persistent inefficiencies
- These inefficiencies manifest differently in bullish/bearish conditions
- A symmetric approach can capture opportunities in both directions
7. **Behavioral Finance Elements**
The 3-bar pattern recognition potentially exploits:
- Herd mentality in trend formation
- Delayed reaction to price momentum
- Cognitive bias in trend confirmation
8. **Quantitative Time Segmentation**
The annual-based period division (training vs testing) implies:
- Market cycles operate on multi-year timeframes
- Strategy robustness requires validation across different market regimes
- Parameter sensitivity needs temporal validation
This strategy combines elements of technical pattern recognition, temporal adaptability, and phased risk management to create a systematic approach to trend exploitation. The theoretical framework suggests markets exhibit persistent but evolving patterns that can be systematically captured through rule-based execution.
Macro-Sentiment Index Model (MSIM)Macro-Sentiment Index Model (MSIM) is a comprehensive trading strategy developed to analyze and interpret the broader macroeconomic and market sentiment. The strategy integrates various quantitative signals, including market volatility, trading volume, market breadth, and economic indicators, to assess the prevailing mood in the financial markets. This sentiment analysis is then used to guide trading decisions, helping identify optimal entry and exit points based on underlying market conditions. The model is specifically designed to capture the shifts in investor sentiment, which have been shown to significantly influence market behavior (Fleming et al., 2001).
The MSIM utilizes a multi-faceted approach to measure sentiment. Drawing from the theory that macroeconomic variables can influence financial markets (Stock & Watson, 2002), the strategy incorporates market volatility (VIX), volume measures, and long-term market trends. These indicators help form a robust view of the market’s risk appetite and potential for price movement. For instance, high volatility often signals increased market uncertainty (Bollerslev, 1986), while volume-based indicators provide insights into investor conviction (Chen, 1991).
Additionally, the model incorporates macroeconomic proxies like GDP growth, interest rates, and unemployment data, leveraging the findings of macroeconomic studies that indicate a direct correlation between these factors and market performance (Hamilton, 1994). By normalizing these economic indicators, the model provides a standardized sentiment score that reflects the aggregated impact of these factors on the market’s outlook.
The MSIM aims to exploit market inefficiencies by responding to shifts in sentiment before they manifest in price movements. Studies have shown that sentiment indicators, such as the Advance-Decline Line and the Stock-Bond Ratio, can be predictive of future price movements (Neely, 2010). The model integrates these indicators into a single composite sentiment score, which is then filtered through momentum signals to refine entry points. This approach is grounded in behavioral finance theory, which suggests that investor sentiment plays a crucial role in driving asset prices, sometimes beyond the reach of fundamental data alone (Shiller, 2000).
The strategy is designed to identify long opportunities when sentiment is particularly favorable, with a focus on minimizing risk during adverse conditions. By analyzing market trends alongside macroeconomic signals, the MSIM helps traders stay aligned with the prevailing market forces.
References:
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Chen, S. S. (1991). The determinants of stock market liquidity. Journal of Financial and Quantitative Analysis, 26(3), 283-305.
• Fleming, M. J., Kirby, C. W., & Ostdiek, B. (2001). The economic value of volatility timing. Journal of Financial and Quantitative Analysis, 36(1), 113-134.
• Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
• Neely, C. J. (2010). The behavior of exchange rates: A survey of recent empirical literature. International Finance Discussion Papers, 981.
• Shiller, R. J. (2000). Irrational Exuberance. Princeton University Press.
• Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147-162.
SPY/TLT Strategy█ STRATEGY OVERVIEW
The "SPY/TLT Strategy" is a trend-following crossover strategy designed to trade the relationship between TLT and its Simple Moving Average (SMA). The default configuration uses TLT (iShares 20+ Year Treasury Bond ETF) with a 20-period SMA, entering long positions on bullish crossovers and exiting on bearish crossunders. **This strategy is NOT optimized and performs best in trending markets.**
█ KEY FEATURES
SMA Crossover System: Uses price/SMA relationship for signal generation (Default: 20-period)
Dynamic Time Window: Configurable backtesting period (Default: 2014-2099)
Equity-Based Position Sizing: Default 100% equity allocation per trade
Real-Time Visual Feedback: Price/SMA plot with trend-state background coloring
Event-Driven Execution: Processes orders at bar close for accurate backtesting
█ SIGNAL GENERATION
1. LONG ENTRY CONDITION
TLT closing price crosses ABOVE SMA
Occurs within specified time window
Generates market order at next bar open
2. EXIT CONDITION
TLT closing price crosses BELOW SMA
Closes all open positions immediately
█ ADDITIONAL SETTINGS
SMA Period: Simple Moving Average length (Default: 20)
Start Time and End Time: The time window for trade execution (Default: 1 Jan 2014 - 1 Jan 2099)
Security Symbol: Ticker for analysis (Default: TLT)
█ PERFORMANCE OVERVIEW
Ideal Market Conditions: Strong trending environments
Potential Drawbacks: Whipsaws in range-bound markets
Backtesting results should be analyzed to optimize the MA Period and EMA Filter settings for specific instruments
