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Strategy vault / Trend following

Quantitative Trend Tracking Strategy Based on Multiple Technical Indicators

This strategy combines multiple technical indicators such as Bollinger Bands, Stochastic Oscillator, and Relative Strength Index to set buy and sell signals for long-term trend tracking operations on crypto assets…

Pine ScriptTrend followingMean reversionRSISMABollinger BandsStochastic
Author
ChaoZhang
Complexity
Starter · 59 lines
Parameters
0
Backtest bar
1m · intraday
Tested on
BTC/USDT · futures
Jump to codeView in vaultOriginal on FMZ

IMG

Overview

This strategy combines multiple technical indicators such as Bollinger Bands, Stochastic Oscillator, and Relative Strength Index to set buy and sell signals for long-term trend tracking operations on crypto assets. The strategy is named "Multi-factor Crypto Quantitative Strategy".

Strategy Principle

The strategy first sets the calculation parameters for indicators like Bollinger Bands, Stochastic Oscillator, and RSI. The buy signal is defined as: close below the Bollinger Lower Band, K line below 20 and above D line, RSI below 30. When all three conditions are met at the same time, go long. The sell signal is partially defined as: K line above 70 and below 70 in the previous period (golden cross dead cross), and there is RSI divergence. When these two conditions are met, close 50% of the position.

Advantage Analysis

This strategy combines multiple indicators to judge the market condition and avoids misjudgment caused by a single indicator. Bollinger Bands to judge whether it is oversold, Stochastic Oscillator to judge whether it is oversold, and RSI to judge whether it is overoversold. The combined effects of multiple indicators can effectively identify market bottoms for accurate long. In addition, the strategy also uses RSI divergence to judge potential trend reversals to avoid late stop loss. Therefore, this strategy can better seize low buy high sell opportunities.

Risk Analysis

This strategy relies on parameter optimization. If the parameters are set improperly, it will fail to correctly identify bottoms and peaks. In addition, there may be incorrect combinations between indicators. For example, Bollinger Bands identify oversold, but other indicators do not reach the corresponding conditions. All these situations can lead to unnecessary losses. Finally, the strategy does not consider the maximum drawdown and position management, which also needs optimization.

Optimization Directions

  1. Test and optimize indicator parameters to find the best parameter combination.

  2. Add maximum drawdown control to pause trading when reaching the threshold.

  3. Add a position management module to dynamically adjust positions based on market conditions. The initial position is smaller and can be increased later.

  4. Add a stop loss strategy. When the market direction is incorrectly determined, set a reasonable stop loss point to control single loss.

Summary

The overall idea of this strategy is clear. Through the judgment of multiple indicators, it has a strong ability to capture bottoms and peaks. But some parameters and modules still have room for optimization. With proper adjustments, it can become a stable profit quantitative strategy.

Source Pine Script

/*backtest
start: 2024-01-14 00:00:00
end: 2024-01-21 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Stratégie d'Entrée et de Sortie Longue", overlay=true)

// Paramètres des indicateurs
longueurBollinger = 20
stdDevBollinger = 2
longueurStochastic = 14
smoothK = 3
smoothD = 3
longueurRSI = 14

// Bollinger Bands
basis = ta.sma(close, longueurBollinger)
dev = ta.stdev(close, longueurBollinger)
lowerBand = basis - stdDevBollinger * dev

// Stochastic Oscillator
k = ta.sma(ta.stoch(close, high, low, longueurStochastic), smoothK)
d = ta.sma(k, smoothD)

// RSI
rsi = ta.rsi(close, longueurRSI)

// Logique des autres indicateurs (à compléter)

// Conditions d'entrée (à définir)
conditionBollinger = close < lowerBand
conditionStochastic = k < 20 and k > d
conditionRSI = rsi < 30
// Autres conditions (Braid Filter, VolumeBIS, Price Density...)

conditionEntree = conditionBollinger and conditionStochastic and conditionRSI // et autres conditions

// Exécution du trade (entrée)
if (conditionEntree)
    strategy.entry("Long Position", strategy.long)

// Conditions de sortie
stochCrossOver70 = k > 70 and k[1] <= 70

// Simplification de la détection de divergence baissière
// (Cette méthode est basique et devrait être raffinée pour une analyse précise)
highsRising = high > high[1]
lowsRising = low > low[1]
rsiFalling = rsi < rsi[1]
divergenceBearish = highsRising and lowsRising and rsiFalling

// Clôturer la moitié de la position
if (stochCrossOver70 and divergenceBearish)
    strategy.close("Long Position", qty_percent = 50)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-22. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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