Leveraged Martingale Futures Trading Strategy

This is a futures trading strategy that leverages the martingale mechanism to achieve high returns. It dynamically adjusts position sizes to increase positions when losing to meet profit targets.

Pine ScriptMartingale Mean reversion Trend following

Author: ChaoZhang
Complexity: Intermediate · 54 lines
Parameters: 8
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This is a futures trading strategy that leverages the martingale mechanism to achieve high returns. It dynamically adjusts position sizes to increase positions when losing to meet profit targets.

Principles

The core logic of this strategy is: when price triggers the stop loss line, reopen positions with larger sizes while lowering the stop loss line by a certain percentage. By increasing position sizes, it aims to lower average entry price. When number of positions reaches the set maximum orders, it waits for price reversal to take profit.

Specifically, it first enters at current price with set position size and take profit/loss levels. When price moves to the stop loss line, larger positions will be reopened and stop loss line is lowered by a set percentage. Such reopening and stop loss lowering operations will lower the average opening price, increasing profit potential. After number of added orders reaches maximum, it waits for price reversal to hit take profit.

Advantage Analysis

The biggest advantage is the ability to lower cost basis through leveraged reopening, while still having the chance of favorable reversal when trends are negative. Also by setting proper stop loss/profit levels, it effectively controls risks.

It also works well for commodities and other high volatility markets, amplifying gains/losses through leverage.

Risk Analysis

Main risk is price may continue downward trend after reopening, even breaking previous stop loss levels, leading to heavy losses. This can be mitigated by setting wider stop loss percentage, smaller leverage ratio etc.

Another risk is insufficient capital to support max order quantity before reversal. It requires adequate funding.

Improvement Areas

Some ways to further optimize the strategy:

Dynamically adjust leverage level, lower when profiting and higher when losing
Incorporate trend indicators to stop loss when trend unclear
Set stop loss width based on market volatility, wider when volatile
Add auto stop loss modules to limit extreme losses

Summary

This is a typical leveraged martingale trading strategy. By lowering cost through added orders, it pursues higher returns but also introduces risks. There is still room for optimization through parameter tuning and feature expansion to suit more market conditions.

Parameters

Name	Default	What it controls
v_input_1	2	Take Profit Percentage
v_input_2	2	Position Size Multiplier
v_input_3	10	Maximum Number of Reinforced Orders
v_input_4	10000	Trade Size in USD
v_input_5	true	Drop Percentage for Next Trade
v_input_6	5	Leverage Factor
v_input_7	true	Enter First Trade at Current Price
v_input_8	0.1	Taker Order Fee Percentage

Source Pine Script

/*backtest
start: 2023-01-19 00:00:00
end: 2024-01-25 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy("Leveraged Martingale Strategy with Fees", overlay=true)

// User-defined input parameters
var float takeProfitPct = input(2.0, title="Take Profit Percentage") / 100.0
var float positionMultiplier = input(2.0, title="Position Size Multiplier")
var int maxOrders = input(10, title="Maximum Number of Reinforced Orders")
var float tradeSizeUSD = input(10000.0, title="Trade Size in USD")
var float dropPctForNextTrade = input(1.0, title="Drop Percentage for Next Trade") / 100.0
var float leverage = input(5.0, title="Leverage Factor")
var bool enterAtCurrentPrice = input(true, title="Enter First Trade at Current Price")
var float takerFeePct = input(0.1, title="Taker Order Fee Percentage") / 100.0

// State variables
var float last_entry_price = na
var float avg_entry_price = na
var float total_position_size = 0.0
var int num_trades = 0

// Entry logic
if (num_trades == 0)
    if (enterAtCurrentPrice or close < last_entry_price * (1 - dropPctForNextTrade))
        float size = tradeSizeUSD / close * leverage
        strategy.entry("Long", strategy.long, qty=size)
        avg_entry_price := close
        total_position_size := size
        last_entry_price := close
        num_trades := 1
else if (close < last_entry_price * (1 - dropPctForNextTrade) and num_trades < maxOrders)
    float size = tradeSizeUSD / close * leverage * pow(positionMultiplier, num_trades)
    strategy.entry("Double Long" + tostring(num_trades), strategy.long, qty=size)
    avg_entry_price := ((avg_entry_price * total_position_size) + (close * size)) / (total_position_size + size)
    total_position_size := total_position_size + size
    last_entry_price := close
    num_trades := num_trades + 1

// Take profit logic adjusted for leverage and fees
var float take_profit_price = na
var float fee_deduction = na
if (num_trades > 0)
    take_profit_price := avg_entry_price * (1 + takeProfitPct / leverage)
    fee_deduction := total_position_size * close * takerFeePct
    if (close > take_profit_price + fee_deduction / total_position_size)
        strategy.close_all()
        num_trades := 0

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-26. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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