Overview
This strategy is a sophisticated trading system that combines multi-timeframe RSI analysis with a dynamic grid trading system. It identifies market overbought and oversold conditions by analyzing RSI values across three different timeframes while using an ATR-based dynamic grid system for position management. The strategy also incorporates daily profit targets and maximum drawdown protection mechanisms to effectively balance returns and risks.

Strategy Principles
The core logic includes several key components:

Multi-timeframe Analysis - Monitors RSI indicators across current, 60-minute, and 240-minute timeframes, triggering trades only when all three periods show overbought or oversold signals.
Dynamic Grid System - Uses ATR as volatility reference to dynamically calculate grid spacing. Increases position size with a multiplier factor when price moves against the position.
Position Management - Uses 1% of account equity as base position size, controlled by lot_multiplier parameter for grid scaling.
Risk Control - Includes daily profit target, 2% account equity maximum drawdown protection, and reverse signal closure mechanism.

Strategy Advantages

Multi-dimensional Signal Confirmation - Reduces false signals through analysis of multiple timeframe RSI indicators.
Flexible Position Management - Dynamic grid system adapts to market volatility.
Comprehensive Risk Control - Daily profit target and maximum drawdown protection effectively manage risk.
Highly Customizable - Multiple adjustable parameters for different market environments.

Strategy Risks

Trend Risk - Grid strategy may face continuous losses in strong trend markets. Consider adding trend filters.
Capital Management Risk - Multiple grids may lead to excessive capital usage. Strictly control maximum grid levels.
Parameter Sensitivity - Strategy performance is sensitive to parameter settings. Thorough parameter optimization testing recommended.

Strategy Optimization Directions

Trend Recognition Enhancement - Add moving averages or other trend indicators as filters.
Dynamic Parameter Adjustment - Automatically adjust RSI thresholds and grid parameters based on market volatility.
Stop Loss Optimization - Implement individual stop loss levels for each grid position.
Time Filtering - Add trading time filters to avoid low liquidity periods.

Summary
This strategy creates a balanced trading approach by combining multi-timeframe RSI analysis with a dynamic grid trading system. Its comprehensive risk control mechanisms and flexible parameter settings make it suitable for various market environments. The strategy's stability and profitability can be further enhanced through the suggested optimization directions.

Strategy source code

/*backtest
start: 2024-02-10 00:00:00
end: 2025-02-08 08:00:00
period: 3h
basePeriod: 3h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Multi-Timeframe RSI Grid Strategy with Arrows", overlay=true)

// Input parameters
rsi_length = input.int(14, "RSI Length")
oversold = input.int(30, "Oversold Level")
overbought = input.int(70, "Overbought Level")
higher_tf1 = input.string("60", "Higher Timeframe 1")
higher_tf2 = input.string("240", "Higher Timeframe 2")
grid_factor = input.float(1.2, "Grid Multiplication Factor", step=0.1)
lot_multiplier = input.float(1.5, "Lot Multiplication Factor", step=0.1)
max_grid = input.int(5, "Maximum Grid Levels")
daily_target = input.float(4.0, "Daily Profit Target (%)", step=0.5)
atr_length = input.int(14, "ATR Length")

// Calculate RSI values
current_rsi = ta.rsi(close, rsi_length)
higher_tf1_rsi = request.security(syminfo.tickerid, higher_tf1, ta.rsi(close, rsi_length))
higher_tf2_rsi = request.security(syminfo.tickerid, higher_tf2, ta.rsi(close, rsi_length))

// Grid system variables
var int grid_level = 0
var float last_entry_price = na
var float base_size = strategy.equity * 0.01 / close
var float daily_profit_target = strategy.equity * (daily_target / 100)
var bool target_reached = false

// ATR for grid spacing
atr = ta.atr(atr_length)
grid_space = atr * grid_factor

// Daily reset
new_day = ta.change(time("D"))
if new_day
    daily_profit_target := strategy.equity * (daily_target / 100)
    target_reached := false
    grid_level := 0
    last_entry_price := na

// Trading conditions
buy_condition = current_rsi < oversold and higher_tf1_rsi < oversold and higher_tf2_rsi < oversold
sell_condition = current_rsi > overbought and higher_tf1_rsi > overbought and higher_tf2_rsi > overbought

// Reverse signal detection
reverse_long_to_short = sell_condition and strategy.position_size > 0
reverse_short_to_long = buy_condition and strategy.position_size < 0

// Close all trades on reverse signals
if reverse_long_to_short or reverse_short_to_long
    strategy.close_all()
    grid_level := 0
    last_entry_price := na

// Grid management logic
if strategy.position_size == 0
    grid_level := 0
    last_entry_price := na

if strategy.position_size > 0 and not reverse_long_to_short
    if close < last_entry_price - grid_space and grid_level < max_grid and not target_reached
        strategy.entry("Long Grid " + str.tostring(grid_level), strategy.long, qty=base_size * math.pow(lot_multiplier, grid_level))
        grid_level += 1
        last_entry_price := close

if strategy.position_size < 0 and not reverse_short_to_long
    if close > last_entry_price + grid_space and grid_level < max_grid and not target_reached
        strategy.entry("Short Grid " + str.tostring(grid_level), strategy.short, qty=base_size * math.pow(lot_multiplier, grid_level))
        grid_level += 1
        last_entry_price := close

// Initial entry
if buy_condition and strategy.position_size == 0 and not target_reached
    strategy.entry("Long", strategy.long, qty=base_size)
    grid_level := 1
    last_entry_price := close

if sell_condition and strategy.position_size == 0 and not target_reached
    strategy.entry("Short", strategy.short, qty=base_size)
    grid_level := 1
    last_entry_price := close

// Profit target check
current_profit = strategy.netprofit + strategy.openprofit
if current_profit >= daily_profit_target and not target_reached
    strategy.close_all()
    target_reached := true

// Drawdown protection
if strategy.openprofit < -(0.02 * strategy.equity)  // 2% drawdown protection
    strategy.close_all()
    grid_level := 0
    last_entry_price := na

// Plot Buy and Sell Arrows
plotshape(series=buy_condition and strategy.position_size == 0, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="BUY", size=size.small)
plotshape(series=sell_condition and strategy.position_size == 0, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL", size=size.small)

// Plotting RSI
plot(current_rsi, "Current RSI", color=color.blue)
plot(higher_tf1_rsi, "HTF1 RSI", color=color.red)
plot(higher_tf2_rsi, "HTF2 RSI", color=color.green)
hline(oversold, "Oversold", color=color.gray)
hline(overbought, "Overbought", color=color.gray)

Strategy Parameters

The original address: Multi-Timeframe Dynamic Grid RSI Trend-Oscillation Trading Strategy