Multi level Indicator Overlapping RSI Trading Strategy

Pine ScriptMean reversion Trend following Volatility RSI

Author: ChaoZhang
Complexity: Starter · 60 lines
Parameters: 0
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a multi-level indicator overlapping trading system based on the Relative Strength Index (RSI). Operating within a specific trading window, it identifies trading opportunities through RSI's overbought and oversold signals, combined with a dynamic position adjustment mechanism that employs a scaled entry approach during adverse market movements. The strategy implements profit-taking based on average entry price targets.

Strategy Principle

The strategy operates based on the following core components:

RSI calculation uses standard 14 periods with closing price as source data
Trading window is controlled between 2-4 hours, adjustable based on market characteristics
Entry signals based on RSI below 30 (oversold) and above 70 (overbought) levels
Position building includes initial position and dynamic adjustment levels
Scaling mechanism triggers when price moves adversely by 1 point
Take profit is set at 1.5 points from average entry price

Strategy Advantages

Multi-level signal filtering: Combines RSI technical indicator and time window dual filtering to effectively reduce false signals
Dynamic position management: Reduces average cost during adverse market movements through scaled entry mechanism
Reasonable risk-reward ratio: Take profit levels based on average entry price ensure overall trade expectancy
Clear strategy logic: Well-defined module responsibilities facilitate subsequent optimization and adjustment
High adaptability: Key parameters can be optimized for different market characteristics

Strategy Risks

Trend market risk: May face excessive capital usage due to frequent scaling in strong trend markets
Time window limitation: Specific time window restrictions might miss good opportunities in other periods
Parameter sensitivity: Settings for RSI period, entry spacing significantly impact strategy performance
Capital management risk: Requires reasonable control of single entry proportion to avoid over-concentration

Strategy Optimization Directions

Introduce trend filter: Suggest adding moving averages or other trend indicators to optimize entry timing
Dynamic parameter optimization: RSI thresholds and entry spacing can be dynamically adjusted based on market volatility
Improve stop loss mechanism: Recommend adding trailing stop loss functionality to better protect existing profits
Optimize time window: Better trading periods can be identified through backtesting data analysis
Add volume indicators: Incorporate volume analysis to improve signal reliability

Summary

The strategy forms a relatively complete trading system through the combination of RSI indicators and scaled entry mechanisms. Its core advantages lie in its multi-level signal filtering mechanism and flexible position management approach, while attention needs to be paid to trend market risks and parameter optimization issues. The overall performance of the strategy can be further improved through enhancements such as adding trend filters and optimizing stop loss mechanisms.

Source Pine Script

/*backtest
start: 2024-12-10 00:00:00
end: 2025-01-08 08:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT","balance":49999}]
*/

//@version=6
strategy("TonyM RSI", overlay=true)

// Input Settings
rsiLengthInput = input.int(14, minval=1, title="RSI Length", group="RSI Settings")
rsiSourceInput = input.source(close, "Source", group="RSI Settings")
startHour = input.int(2, "Start Hour", minval=0, maxval=23, group="Trading Window")
endHour = input.int(4, "End Hour", minval=0, maxval=23, group="Trading Window")

// RSI Calculation
change = ta.change(rsiSourceInput)
up = ta.rma(math.max(change, 0), rsiLengthInput)
down = ta.rma(-math.min(change, 0), rsiLengthInput)
rsi = down == 0 ? 100 : up == 0 ? 0 : 100 - (100 / (1 + up / down))

// Time Filter
inTradingWindow = (hour >= startHour and hour < endHour)

// Strategy Settings
buyLevel = 30
sellLevel = 70
scaleDistance = 1.0  // Distance in points to add to the position
takeProfitPoints = 1.5  // Profit target from average price
initialQty = 1  // Initial trade size
scalingQty = 1  // Additional trade size for scaling

// Trade Logic
if inTradingWindow
    // Entry Logic
    if rsi <= buyLevel and strategy.position_size == 0
        strategy.entry("Buy", strategy.long, qty=initialQty)
    if rsi >= sellLevel and strategy.position_size == 0
        strategy.entry("Sell", strategy.short, qty=initialQty)

    // Scaling Logic
    if strategy.position_size > 0 and close <= strategy.position_avg_price - scaleDistance
        strategy.entry("Scale Buy", strategy.long, qty=scalingQty)
    if strategy.position_size < 0 and close >= strategy.position_avg_price + scaleDistance
        strategy.entry("Scale Sell", strategy.short, qty=scalingQty)

    // Exit Logic (based on average price)
    if strategy.position_size > 0
        strategy.exit("Take Profit Long", "Buy", limit=strategy.position_avg_price + takeProfitPoints)
    if strategy.position_size < 0
        strategy.exit("Take Profit Short", "Sell", limit=strategy.position_avg_price - takeProfitPoints)

// Plot RSI
plot(rsi, "RSI", color=color.blue, linewidth=1)
rsiUpperBand = hline(70, "RSI Upper Band", color=color.red)
rsiLowerBand = hline(30, "RSI Lower Band", color=color.green)
fill(rsiUpperBand, rsiLowerBand, color=color.new(color.gray, 90))

Published by ChaoZhang on the FMZ strategy square · last modified 2025-01-10. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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