Golden Cross VWMA & EMA 4h PinescriptlabsThis strategy combines the 50-period Volume-Weighted Moving Average (VWMA) on the current timeframe with a 200-period Simple Moving Average (SMA) on the 4-hour timeframe. This combination of indicators with different characteristics and time horizons aims to identify strong and sustained trends across multiple timeframes.
The VWMA is a variant of the moving average that assigns greater weight to periods of higher volatility, helping to avoid misleading signals. On the other hand, the 4-hour SMA is used as an additional trend filter in a shorter-term horizon. By combining these two indicators, the strategy can leverage the strength of the VWMA to capture the main trend, but only when confirmed by the SMA in the lower timeframe.
Buy signals are generated when the VWMA crosses above the 4-hour SMA, indicating a potential bullish trend aligned in both timeframes. Sell signals occur on a bearish cross, suggesting a possible reversal of the main trend.
The default parameters are a 50-period VWMA and a 200-period 4-hour SMA. It is recommended to adjust these lengths according to the traded instrument and the desired timeframe. It is also crucial to use stop losses and profit targets to properly manage risk.
By combining indicators of different types and timeframes, this strategy aims to provide a more comprehensive view of trend strength.
Español:
Esta estrategia combina la Volume-Weighted Moving Average (VWMA) de 50 períodos en el timeframe actual con una Simple Moving Average (SMA) de 200 períodos en el timeframe de 4 horas. Esta combinación de indicadores de distinta naturaleza y horizontes temporales busca identificar tendencias fuertes y sostenidas en múltiples timeframes.
La VWMA es una variante de la media móvil que asigna mayor ponderación a los períodos de mayor volatilidad, lo que ayuda a evitar señales engañosas. Por otro lado, la SMA de 4 horas se utiliza como un filtro adicional de tendencia en un horizonte de corto plazo. Al combinar estos dos indicadores, la estrategia puede aprovechar la fortaleza de la VWMA para capturar la tendencia principal, pero sólo cuando es confirmada por la SMA en el timeframe menor.
Las señales de compra se generan cuando la VWMA cruza al alza la SMA de 4 horas, indicando una potencial tendencia alcista alineada en ambos horizontes temporales. Las señales de venta ocurren en el cruce bajista, sugiriendo una posible reversión de la tendencia principal.
Los parámetros predeterminados son: VWMA de 50 períodos y SMA de 4 horas de 200 períodos. Se recomienda ajustar estas longitudes según el instrumento operado y el horizonte temporal deseado. También es crucial utilizar stops y objetivos de ganancias para controlar adecuadamente el riesgo.
Al combinar indicadores de diferentes tipos y timeframes, esta estrategia busca brindar una visión más completa de la fuerza de la tendencia.
Promedio móvil ponderado por volumen (VWMA)
Multi-TF AI SuperTrend with ADX - Strategy [PresentTrading]
## █ Introduction and How it is Different
The trading strategy in question is an enhanced version of the SuperTrend indicator, combined with AI elements and an ADX filter. It's a multi-timeframe strategy that incorporates two SuperTrends from different timeframes and utilizes a k-nearest neighbors (KNN) algorithm for trend prediction. It's different from traditional SuperTrend indicators because of its AI-based predictive capabilities and the addition of the ADX filter for trend strength.
BTC 8hr Performance
ETH 8hr Performance
## █ Strategy, How it Works: Detailed Explanation (Revised)
### Multi-Timeframe Approach
The strategy leverages the power of multiple timeframes by incorporating two SuperTrend indicators, each calculated on a different timeframe. This multi-timeframe approach provides a holistic view of the market's trend. For example, a 8-hour timeframe might capture the medium-term trend, while a daily timeframe could capture the longer-term trend. When both SuperTrends align, the strategy confirms a more robust trend.
### K-Nearest Neighbors (KNN)
The KNN algorithm is used to classify the direction of the trend based on historical SuperTrend values. It uses weighted voting of the 'k' nearest data points. For each point, it looks at its 'k' closest neighbors and takes a weighted average of their labels to predict the current label. The KNN algorithm is applied separately to each timeframe's SuperTrend data.
### SuperTrend Indicators
Two SuperTrend indicators are used, each from a different timeframe. They are calculated using different moving averages and ATR lengths as per user settings. The SuperTrend values are then smoothed to make them suitable for KNN-based prediction.
### ADX and DMI Filters
The ADX filter is used to eliminate weak trends. Only when the ADX is above 20 and the directional movement index (DMI) confirms the trend direction, does the strategy signal a buy or sell.
### Combining Elements
A trade signal is generated only when both SuperTrends and the ADX filter confirm the trend direction. This multi-timeframe, multi-indicator approach reduces false positives and increases the robustness of the strategy.
By considering multiple timeframes and using machine learning for trend classification, the strategy aims to provide more accurate and reliable trade signals.
BTC 8hr Performance (Zoom-in)
## █ Trade Direction
The strategy allows users to specify the trade direction as 'Long', 'Short', or 'Both'. This is useful for traders who have a specific market bias. For instance, in a bullish market, one might choose to only take 'Long' trades.
## █ Usage
Parameters: Adjust the number of neighbors, data points, and moving averages according to the asset and market conditions.
Trade Direction: Choose your preferred trading direction based on your market outlook.
ADX Filter: Optionally, enable the ADX filter to avoid trading in a sideways market.
Risk Management: Use the trailing stop-loss feature to manage risks.
## █ Default Settings
Neighbors (K): 3
Data points for KNN: 12
SuperTrend Length: 10 and 5 for the two different SuperTrends
ATR Multiplier: 3.0 for both
ADX Length: 21
ADX Time Frame: 240
Default trading direction: Both
By customizing these settings, traders can tailor the strategy to fit various trading styles and assets.
Double AI Super Trend Trading - Strategy [PresentTrading]█ Introduction and How It is Different
The Double AI Super Trend Trading Strategy is a cutting-edge approach that leverages the power of not one, but two AI algorithms, in tandem with the SuperTrend technical indicator. The strategy aims to provide traders with enhanced precision in market entry and exit points. It is designed to adapt to market conditions dynamically, offering the flexibility to trade in both bullish and bearish markets.
*The KNN part is mainly referred from @Zeiierman.
BTCUSD 8hr performance
ETHUSD 8hr performance
█ Strategy, How It Works: Detailed Explanation
1. SuperTrend Calculation
The SuperTrend is a popular indicator that captures market trends through a combination of the Volume-Weighted Moving Average (VWMA) and the Average True Range (ATR). This strategy utilizes two sets of SuperTrend calculations with varying lengths and factors to capture both short-term and long-term market trends.
2. KNN Algorithm
The strategy employs k-Nearest Neighbors (KNN) algorithms, which are supervised machine learning models. Two sets of KNN algorithms are used, each focused on different lengths of historical data and number of neighbors. The KNN algorithms classify the current SuperTrend data point as bullish or bearish based on the weighted sum of the labels of the k closest historical data points.
3. Signal Generation
Based on the KNN classifications and the SuperTrend indicator, the strategy generates signals for the start of a new trend and the continuation of an existing trend.
4. Trading Logic
The strategy uses these signals to enter long or short positions. It also incorporates dynamic trailing stops for exit conditions.
Local picture
█ Trade Direction
The strategy allows traders to specify their trading direction: long, short, or both. This enables the strategy to be versatile and adapt to various market conditions.
█ Usage
ToolTips: Comprehensive tooltips are provided for each parameter to guide the user through the customization process.
Inputs: Traders can customize numerous parameters including the number of neighbors in KNN, ATR multiplier, and types of moving averages.
Plotting: The strategy also provides visual cues on the chart to indicate bullish or bearish trends.
Order Execution: Based on the generated signals, the strategy will execute buy or sell orders automatically.
█ Default Settings
The default settings are configured to offer a balanced approach suitable for most scenarios:
Initial Capital: $10,000
Default Quantity Type: 10% of equity
Commission: 0.1%
Slippage: 1
Currency: USD
These settings can be modified to suit various trading styles and asset classes.
AI SuperTrend - Strategy [presentTrading]
█ Introduction and How it is Different
The AI Supertrend Strategy is a unique hybrid approach that employs both traditional technical indicators and machine learning techniques. Unlike standard strategies that rely solely on traditional indicators or mathematical models, this strategy integrates the power of k-Nearest Neighbors (KNN), a machine learning algorithm, with the tried-and-true SuperTrend indicator. This blend aims to provide traders with more accurate, responsive, and context-aware trading signals.
*The KNN part is mainly referred from @Zeiierman.
BTCUSD 8hr performance
ETHUSD 8hr performance
█ Strategy, How it Works: Detailed Explanation
SuperTrend Calculation
Volume-Weighted Moving Average (VWMA): A VWMA of the close price is calculated based on the user-defined length (len). This serves as the central line around which the upper and lower bands are calculated.
Average True Range (ATR): ATR is calculated over a period defined by len. It measures the market's volatility.
Upper and Lower Bands: The upper band is calculated as VWMA + (factor * ATR) and the lower band as VWMA - (factor * ATR). The factor is a user-defined multiplier that decides how wide the bands should be.
KNN Algorithm
Data Collection: An array (data) is populated with recent n SuperTrend values. Corresponding labels (labels) are determined by whether the weighted moving average price (price) is greater than the weighted moving average of the SuperTrend (sT).
Distance Calculation: The absolute distance between each data point and the current SuperTrend value is calculated.
Sorting & Weighting: The distances are sorted in ascending order, and the closest k points are selected. Each point is weighted by the inverse of its distance to the current point.
Classification: A weighted sum of the labels of the k closest points is calculated. If the sum is closer to 1, the trend is predicted as bullish; if closer to 0, bearish.
Signal Generation
Start of Trend: A new bullish trend (Start_TrendUp) is considered to have started if the current trend color is bullish and the previous was not bullish. Similarly for bearish trends (Start_TrendDn).
Trend Continuation: A bullish trend (TrendUp) is considered to be continuing if the direction is negative and the KNN prediction is 1. Similarly for bearish trends (TrendDn).
Trading Logic
Long Condition: If Start_TrendUp or TrendUp is true, a long position is entered.
Short Condition: If Start_TrendDn or TrendDn is true, a short position is entered.
Exit Condition: Dynamic trailing stops are used for exits. If the trend does not continue as indicated by the KNN prediction and SuperTrend direction, an exit signal is generated.
The synergy between SuperTrend and KNN aims to filter out noise and produce more reliable trading signals. While SuperTrend provides a broad sense of the market direction, KNN refines this by predicting short-term price movements, leading to a more nuanced trading strategy.
Local picture
█ Trade Direction
The strategy allows traders to choose between taking only long positions, only short positions, or both. This is particularly useful for adapting to different market conditions.
█ Usage
ToolTips: Explains what each parameter does and how to adjust them.
Inputs: Customize values like the number of neighbors in KNN, ATR multiplier, and moving average type.
Plotting: Visual cues on the chart to indicate bullish or bearish trends.
Order Execution: Based on the generated signals, the strategy will execute buy/sell orders.
█ Default Settings
The default settings are selected to provide a balanced approach, but they can be modified for different trading styles and asset classes.
Initial Capital: $10,000
Default Quantity Type: 10% of equity
Commission: 0.1%
Slippage: 1
Currency: USD
By combining both machine learning and traditional technical analysis, this strategy offers a sophisticated and adaptive trading solution.
Advanced VWAP_Pullback Strategy_Trend-Template QualifierGeneral Description and Unique Features of this Script
Introducing the Advanced VWAP Momentum-Pullback Strategy (long-only) that offers several unique features:
1. Our script/strategy utilizes Mark Minervini's Trend-Template as a qualifier for identifying stocks and other financial securities in confirmed uptrends. Mark Minervini, a 2x US Investment Champion, developed the Trend-Template, which covers eight different and independent characteristics that can be adjusted and optimized in this trend-following strategy to ensure the best results. The strategy will only trigger buy-signals in case the optimized qualifiers are being met.
2. Our strategy is based on the supply/demand balance in the market, making it timeless and effective across all timeframes. Whether you are day trading using 1- or 5-min charts or swing-trading using daily charts, this strategy can be applied and works very well.
3. We have also integrated technical indicators such as the RSI and the MA / VWAP crossover into this strategy to identify low-risk pullback entries in the context of confirmed uptrends. By doing so, the risk profile of this strategy and drawdowns are being reduced to an absolute minimum.
Minervini’s Trend-Template and the ‘Stage-Analysis’ of the Markets
This strategy is a so-called 'long-only' strategy. This means that we only take long positions, short positions are not considered.
The best market environment for such strategies are periods of stable upward trends in the so-called stage 2 - uptrend.
In stable upward trends, we increase our market exposure and risk.
In sideways markets and downward trends or bear markets, we reduce our exposure very quickly or go 100% to cash and wait for the markets to recover and improve. This allows us to avoid major losses and drawdowns.
This simple rule gives us a significant advantage over most undisciplined traders and amateurs!
'The Trend is your Friend'. This is a very old but true quote.
What's behind it???
• 98% of stocks made their biggest gains in a Phase 2 upward trend.
• If a stock is in a stable uptrend, this is evidence that larger institutions are buying the stock sustainably.
• By focusing on stocks that are in a stable uptrend, the chances of profit are significantly increased.
• In a stable uptrend, investors know exactly what to expect from further price developments. This makes it possible to locate low-risk entry points.
The goal is not to buy at the lowest price – the goal is to buy at the right price!
Each stock goes through the same maturity cycle – it starts at stage 1 and ends at stage 4
Stage 1 – Neglect Phase – Consolidation
Stage 2 – Progressive Phase – Accumulation
Stage 3 – Topping Phase – Distribution
Stage 4 – Downtrend – Capitulation
This strategy focuses on identifying stocks in confirmed stage 2 uptrends. This in itself gives us an advantage over long-term investors and less professional traders.
By focusing on stocks in a stage 2 uptrend, we avoid losses in downtrends (stage 4) or less profitable consolidation phases (stages 1 and 3). We are fully invested and put our money to work for us, and we are fully invested when stocks are in their stage 2 uptrends.
But how can we use technical chart analysis to find stocks that are in a stable stage 2 uptrend?
Mark Minervini has developed the so-called 'trend template' for this purpose. This is an essential part of our JS-TechTrading pullback strategy. For our watchlists, only those individual values that meet the tough requirements of Minervini's trend template are eligible.
The Trend Template
• 200d MA increasing over a period of at least 1 month, better 4-5 months or longer
• 150d MA above 200d MA
• 50d MA above 150d MA and 200d MA
• Course above 50d MA, 150d MA and 200d MA
• Ideally, the 50d MA is increasing over at least 1 month
• Price at least 25% above the 52w low
• Price within 25% of 52w high
• High relative strength according to IBD.
NOTE: In this basic version of the script, the Trend-Template has to be used as a separate indicator on TradingView (Public Trend-Template indicators are available in TradingView – community scripts). It is recommended to only execute buy signals in case the stock or financial security is in a stage 2 uptrend, which means that the criteria of the trend-template are fulfilled.
This strategy can be applied to all timeframes from 5 min to daily.
The VWAP Momentum-Pullback Strategy
For the JS-TechTrading VWAP Momentum-Pullback Strategy, only stocks and other financial instruments that meet the selected criteria of Mark Minervini's trend template are recommended for algorithmic trading with this startegy.
A further prerequisite for generating a buy signals is that the individual value is in a short-term oversold state (RSI).
When the selling pressure is over and the continuation of the uptrend can be confirmed by the MA / VWAP crossover after reaching a price low, a buy signal is issued by this strategy.
Stop-loss limits and profit targets can be set variably. You also have the option to make use of the trailing stop exit strategy.
Relative Strength Index (RSI)
The Relative Strength Index (RSI) is a technical indicator developed by Welles Wilder in 1978. The RSI is used to perform a market value analysis and identify the strength of a trend as well as overbought and oversold conditions. The indicator is calculated on a scale from 0 to 100 and shows how much an asset has risen or fallen relative to its own price in recent periods.
The RSI is calculated as the ratio of average profits to average losses over a certain period of time. A high value of the RSI indicates an overbought situation, while a low value indicates an oversold situation. Typically, a value > 70 is considered an overbought threshold and a value < 30 is considered an oversold threshold. A value above 70 signals that a single value may be overvalued and a decrease in price is likely , while a value below 30 signals that a single value may be undervalued and an increase in price is likely.
For example, let's say you're watching a stock XYZ. After a prolonged falling movement, the RSI value of this stock has fallen to 26. This means that the stock is oversold and that it is time for a potential recovery. Therefore, a trader might decide to buy this stock in the hope that it will rise again soon.
The MA / VWAP Crossover Trading Strategy
This strategy combines two popular technical indicators: the Moving Average (MA) and the Volume Weighted Average Price (VWAP). The MA VWAP crossover strategy is used to identify potential trend reversals and entry/exit points in the market.
The VWAP is calculated by taking the average price of an asset for a given period, weighted by the volume traded at each price level. The MA, on the other hand, is calculated by taking the average price of an asset over a specified number of periods. When the MA crosses above the VWAP, it suggests that buying pressure is increasing, and it may be a good time to enter a long position. When the MA crosses below the VWAP, it suggests that selling pressure is increasing, and it may be a good time to exit a long position or enter a short position.
Traders typically use the MA VWAP crossover strategy in conjunction with other technical indicators and fundamental analysis to make more informed trading decisions. As with any trading strategy, it is important to carefully consider the risks and potential rewards before making any trades.
This strategy is applicable to all timeframes and the relevant parameters for the underlying indicators (RSI and MA/VWAP) can be adjusted and optimized as needed.
Backtesting
Backtesting gives outstanding results on all timeframes and drawdowns can be reduced to a minimum level. In this example, the hourly chart for MCFT has been used.
Settings for backtesting are:
- Period from Jan 2020 until March 2023
- Starting capital 100k USD
- Position size = 25% of equity
- 0.01% commission = USD 2.50.- per Trade
- Slippage = 2 ticks
Other comments
- 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 Trend-Template and the RSI 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.
VWMA/SMA 3Commas BotThis strategy utilizes two pairs of different Moving Averages, two Volume-Weighted Moving Averages (VWMA) and two Simple Moving Averages (SMA).
There is a FAST and SLOW version of each VWMA and SMA.
The concept behind this strategy is that volume is not taken into account when calculating a Simple Moving Average.
Simple Moving Averages are often used to determine the dominant direction of price movement and to help a trader look past any short-term volatility or 'noise' from price movement, and instead determine the OVERALL direction of price movement so that one can trade in that direction (trend-following) or look for opportunities to trade AGAINST that direction (fading).
By comparing the different movements of a Volume-Weighted Moving Average against a Simple Moving Average of the same length, a trader can get a better picture of what price movements are actually significant, helping to reduce false signals that might occur from only using Simple Moving Averages.
The practical applications of this strategy are identifying dominant directional trends. These can be found when the Volume Weighted Moving Average is moving in the same direction as the Simple Moving Average, and ideally, tracking above it.
This would indicate that there is sufficient volume supporting an uptrend or downtrend, and thus gives traders additional confirmation to potentially look for a trade in that direction.
One can initially look for the Fast VWMA to track above the Fast SMA as your initial sign of bullish confirmation (reversed for downtrending markets). Then, when the Fast VWMA crosses over the Slow SMA, one can determine additional trend strength. Finally, when the Slow VWMA crosses over the Slow SMA, one can determine that the trend is truly strong.
Traders can choose to look for trade entries at either of those triggers, depending on risk tolerance and risk appetite.
Furthermore, this strategy can be used to identify divergence or weakness in trending movements. This is very helpful for identifying potential areas to exit one's trade or even look for counter-trend trades (reversals).
These moments occur when the Volume-Weighted Moving Average, either fast or slow, begins to trade in the opposite direction as their Simple Moving Average counterpart.
For instance, if price has been trending upwards for awhile, and the Fast VWMA begins to trade underneath the Fast SMA, this is an indication that volume is beginning to falter. Uptrends need appropriate volume to continue moving with momentum, so when we see volume begin to falter, it can be a potential sign of an upcoming reversal in trend.
Depending on how quickly one wants to enter into a movement, one could look for crosses of the Fast VWMA under/over the Fast SMA, crosses of the Fast VWMA over/under the Slow SMA, or crosses over/under of the Slow VWMA and the Slow SMA.
This concept was originally published here on TradingView by ProfitProgrammers.
Here is a link to his original indicator script:
I have added onto this concept by:
converting the original indicator into a strategy tester for backtesting
adding the ability to conveniently test long or short strategies, or both
adding the ability to calculate dynamic position sizes
adding the ability to calculate dynamic stop losses and take profit levels using the Average True Range
adding the ability to exit trades based on overbought/oversold crosses of the Stochastic RSI
conveniently switch between different thresholds or speeds of the Moving Average crosses to test different strategies on different asset classes
easily hook this strategy up to 3Commas for automation via their DCA bot feature
Full credit to ProfitProgrammers for the original concept and idea.
Any feedback or suggestions are greatly appreciated.
Swing Trading SPX CorrelationThis is a long timeframe script designed to benefit from the correlation with the Percentage of stocks Above 200 moving average from SPX
At the same time with this percentage we are creating a weighted moving average to smooth its accuracy.
The rules are simple :
If the moving average is increasing its a long signal/short exit
If the moving average is decreased its a short signal/long exit.
Curently the strategy has been adapted for long only entries.
If you have any questions let me know !
Profitable Contrarian scalpingUses the 5 period and 10 period VMWAs that have been smoothed with a 5 period SMA of the close price. Normally, a short crossover long formation signals a buy signal, but as scalpers know, the 1 minute chart moves so fast and with so much volatility that lagging indicators get wrecked by the market. According, this strategy operates under the assumption that by the time this lagging indicator makes a signal, the price is ready to reverse. Losses are taken swiftly in the case of a continuation pattern. This indicator averages a 55-65% profitable rate and is almost always a positive P/L on the 1 minute chart of the most commonly traded assets.
Of course, there may be validity for this indicator outside the 1 minute chart, but I have found such success to be very limited. Accordingly, use this indicator on SPY, TQQQ, TSLA, AMZN, and major cryptos on the 1 min chart.
TriautoETF(TQQQ) Short Strategy B1○ Objective.
This is a strategy for the TQQQ NASDAQ:TQQQ short strategy in the TriAuto ETF .
It is used as a hedging short rather than for profit-making purposes.
Entry and close points are indicated.
○ Strategy
The strategy is to hold a short position when the price falls below the moving average line, which is a market-conscious line that is rarely broken.
The close (settlement) is determined by using the moving average.
The moving average is based on the market-conscious QQQ NASDAQ:QQQ .
This script is used on the daily chart of the TQQQ.
It works as a hedge for long positions because open interest is held even at the major bottoms of the China and Corona shocks.
The system is set up to quickly cut its losses even if the moving average is "tricked" into falling below the moving average.
Configurable Multi MA Crossover Voting SystemThis strategy goes long when all fast moving averages that you have defined are above their counterpart slow moving averages.
Long position is closed when profit or loss target is hit and at least one of the fast moving averages is below its counterpart slow moving average.
The format of the config is simple. The format is : FASTxSLOW,FASTxSLOW,...
Example : If you want 2 moving averages fast=9,slow=14 and fast=20,slow=50 you define it like this : 9x14,20x50
Another example : 5x10,10x15,15x20 => means 3 moving average setups : first wih fast=5/slow=10, second with fast=10/slow=15, last with fast=15/slow=20
You can chose the type of moving average : SMA, WMA, VWMA (i got issues with EMA/RMA so i removed them)
You can chose the source of the moving average : high, close, hl2 etc.
You can chose the period on which ATR is calculated and ATR profit/loss factors.
Profit is calculated like : buy_price + atr_factor*atr
Loss is calculated like : buy_price - atr_factor*atr
Performance in backtest is variable depending on the timeframe, the options and the market.
Performance in backtest suggests it works better for higher timeframes like 1d, 4h etc.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
Monthly Returns in Strategies with Market BenchmarkThis is a modified version of this excellent script Monthly Returns in PineScript Strategues by QuantNomad
I liked and used the script but wanted to see how strategy performed vs market on each month/year. So I am sharing back.
The modification consists in adding Market or Buy & Hold performance between parenthesis inside each cell to better see how strategy performed vs market.
Also, 3 red levels and 3 green levels have been used :
For green :
1/ Light when strategy pnl > 0 but < market
2/ medium when strategy pnl > 0 and > market
3/ Dark when strategy pnl > 0 and market < 0 or pnl > market x 2
Same logic in the opposite direction for red.
The strategy provided here is just a showcase of how to use the table in pine script.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
VWAP Stoch Long Trailing Stop without wednesday and thursdaySimple trading strategy based on VWAP and Stochastic indicators and a 3% trailing stop.
After backtesting, wednesdays and thursdays seemed to be bad entry days so they are blacklisted.
Kifier's MFI/STOCH Hidden Divergence/Trend BeaterMFI/STOCH Hidden Divergence/Trend Beater
General Idea:
My premise around this strategy was to make a general strategy for crypto that would help out with finding entry positions for when you’re bullish on a crypto and want to hold on for a while, and at the same time avoiding massive drops. Essentially a way to mix long term/ swing trading; I somewhat achieved my goal however it still requires a lot of logic tuning of the trend averages.
I’m a huge proponent of volume indicators and coupled with average closing price, I think this gives a really good idea of what is happening with the market. It gives an idea on the market and retail investor sentiment. This generally gives you logical entry positions (Although I don’t know how amazing that will work with all cryptos, there’s a fine line between a good strategy and one that just rides bubble market conditions, some would argue that’s still a success and others not)
How it works:
There are many components to the strategy that try to do different things:
First of all there are two types of entries, a MFI hidden divergence with a STOCH check, essentially it will only fire when a divergence is detected while STOCH is above 50%, however this might be changed in the future as due to the volatile nature of cryptos, the STOCH is not too effective. The second entry is a simple MFI/STOCH trend, if STOCH is above 50% and the trend is detected to be in a trending long, once a MFI crossover over the 50% line is detected an entry is placed, this is designed to get out profit where the divergence would otherwise be less accurate during strongly trending conditions.
-MFI is a great indicator, as a volume weighted momentum indicator I find it the most accurate of all, the STOCH however is a great indicator to get a general picture of simple market conditions and can filter out the emotional noise of retail investors.
-VWMA and an SMA (The bottom oscillator) gives an idea of the trend tacking into account of the volume, this serves as a more short term filter of the trend for filters.
-OBV checks are done between the OBV and an EMA of the OBV, to get the idea of a volume weighted long trend, which is important for crypto as there are massive rallies to go up due to retail greed, it’s great to jump onto it at the beginning, and get off before the stack of cards fall apart.
-ATR is used to detect when the market is relatively just ranging or moving sideways, which is where the hidden divergence entries are done, during predictable and profitable market conditions.
- Stop loss is based on the closest support of the entry, this is a nice medium of room to breath but also an actual stop loss.
Future plans and improvements:
Currently there’s a lot I want to improve, mostly the divergence detection and the overall sharpe ratio could be much better, but the current value of 0.5 gives me hope that the strategy is onto something. I also want to change TP from a percentage stop to something more dynamic but that might be too optimistic. The current plan is to paper trade test this either by manual or by a python bot, to see how it performs with some user input as well.
VWMA with kNN Machine Learning: MFI/ADXThis is an experimental strategy that uses a Volume-weighted MA (VWMA) crossing together with Machine Learning kNN filter that uses ADX and MFI to predict, whether the signal is useful. k-nearest neighbours (kNN) is one of the simplest Machine Learning classification algorithms: it puts input parameters in a multidimensional space, and then when a new set of parameters are given, it makes a prediction based on plurality vote of its k neighbours.
Money Flow Index (MFI) is an oscillator similar to RSI, but with volume taken into account. Average Directional Index (ADX) is an indicator of trend strength. By putting them together on two-dimensional space and checking, whether nearby values have indicated a strong uptrend or downtrend, we hope to filter out bad signals from the MA crossing strategy.
This is an experiment, so any feedback would be appreciated. It was tested on BTC/USDT pair on 5 minute timeframe. I am planning to expand this strategy in the future to include more moving averages and filters.
Yusram Mount MaV - Day MaV CrossOver Strgty
This indicator shows the comparison between the 7-day fast simple average and the monthly slow average of 5 bars. The red line indicates the monthly green and blue lines the daily average. If the Green-Blue line crosses the red upwards, it is a buy signal and the opposite is a sell signal. As soon as it turns green blue without waiting for the sell signal, a sell signal is created. If you are trading fast, you can consider turning green to blue as an opportunity. In the long run, the red intersection can be interpreted as a Stop point.
I hope it will be useful to everyone.
You can also find the strategy indicator with the same name.
I got the name of this indicator from my daughter's name. The meaning of the name Yüsra means "convenience". I hope this indicator will help you.Yüsram Mount HO - Day HO
Wave Trend w/ VWMA overlayThis is a trend-following strategy and indicator which combines the Wave Trend Strategy (Lazy Bear) by thomas.gigure with the cRSI + Waves Strategy with VWMA overlay by Dr_Roboto .
You may update the parameters of the Wave Trend oscillator or the VWMA indicator to match your own preferences. You may also adjust the Base Quantity used for determining trade size (as described below) to suit your account size and risk tolerance.
The strategy identifies potential signals based on the on the Wave Trend oscillator, originally ported to TradingView by LazyBear. When a signal is produced by the Wave Trend oscillator, trade size is determined by the VWMA.
When the VWMA is trending against the direction of the Wave Trend signal, Base Quantity x 1 is used
When the VWMA is trending neutral, Base Quantity x 2 is used
When the VWMA is trending with the direction of the Wave Trend signal, Base Quantity x 4 is used
The strategy includes the ability to limit trade signals to certain defined periods of time ("Sessions") during the trading day and, optionally, to close any open position at the end of either or both "Sessions." This may be enabled/disabled via the Limit Signals to Trading Sessions? option on the "Inputs" tab of the strategy's "Settings" window.
If you are trading on a daily chart (or longer) you must disable the Limit Signals to Trading Sessions? in order for the strategy to produce signals.
HiLo Extension This Strategy is finding high and low breaks of the day and enter into the trader based on RSI value and time value
1) This strategy is created for Indian Index like Nifty, Bank Nifty and so...
2) Trades are initiate only after 10:15 AM and before 3:10PM
3) High and Low of the day break will be check during the above time frame
4) RSI value will be check (RSI 50)
5) and trade will be initiate
6) Stop loss set as vwma 20...
Note: This Script will work fine in Index future chart not index spot chart...
This is just my idea only... Please back test yourselve, before using it..
Your comments are welcome!
VWMA_withATRstops_strategyThis strategy follows the trend when price is above VWMA indicator. I have modified entry and exit rules to get most out of it.
Instead of entering LONG when price crosses above VWMA, I have used RSI(14) of VWMA . that way it skips the false signals. (some extent)
ENTRY
========
1. VWMA setting is 33
2. When RSIofVwma is above 30 enter Long ( and also checks if price already broke the ATR Stop above line )
Stop Loss and Exits
==================
1. Exit is when price breaks the ATR stop loss
2. ATR setting is set as same VWMA Length and multiplier is 3.5
3. STOP Loss that I mentioned in the settings is being used to calculate the how many units can be purchased based on risk of capital value.
Note: There is NO hard stop loss. having above ATR stop loss works as Trailing stop loss
Warning
=======
For the educational purposes only
EVWBB Strategy [QuantNomad]It's my new strategy using EVWMA (Elastic Volume Weighted Moving Average).
Now I created a Bollinger Bands strategy where basis level is EVWMA.
It looks pretty interesting but you have to be careful with the entries/exits on the same bar, I'm using stop orders, so on big moves it happens pretty often.
In the next version, I will try to eliminate these issues.
Entry for this strategy happens when price crossover upper for long and lower for short. I exit both short and long on basis level.
EVWMA VWAP Cross Strategy [QuantNomad]Continue to experiement with VWAP and EVWMA.
It seems that just simple crosses between VWAP and EVWMA can be pretty good signals. VWAP is a bit choppy so you can use VWAP smoothing input to smoth it a bit.
Here are few other strategies based on EVWMA:
EVWMA VWAP MACD Strategy
QuantNomad - EVWMA MACD Strategy
EVWMA VWAP MACD Strategy [QuantNomad]Based on comment of @coondawg71 I tried to compare VWAP and EVWMA.
Both are sort of moving averages so I decided to create a MACD based on these 2 indicators.
In parameters you can set EVWMA Length and 2 smoothing lengths for "macd" and "signal".
Strategy seems to work pretty good at 2h-8h timeframes for crypto.
What do you thing about it?
QuantNomad - EVWMA MACD StrategyPretty simple EVWMA (Elastic Volume Weighted Moving Average ) MACD Strategy.
EVWMA is a quite interesting moving average where the period of the MA is defined from the volume itself.
It incorporates volume information in a natural and logical way. The EVWMA can be looked at as an approximation to the average price paid per share.
As a volume period, you can use sum of the last x bars volumes.
Here are other EVWMA indicators/strategies:
EVWMA indicator:
EVWMA Cross strategy:
QuantNomad - EVWMA Cross StrategyPretty simple EVWMA (Elastic Volume Weighted Moving Average) Cross Strategy. Long on bullish cross, Short on Bearish Crosss.
EVWMA is a quite interesting moving average where period of the MA is defined from volume itself.
It incorporates volume information in a natural and logical way. The eVWMA can be looked at as an approximation to the average price paid per share.
As a volume period you can use sum of the last x bars volumes.
Here is EVWMA as an indicator:
