Mean Reversion Reverse Strategy Based on Moving Average

The strategy is named "Mean Reversion Reverse Strategy Based on Moving Average". The main idea is to buy when price breaks through key moving averages and take profit when reaching preset targets. The main principle…

Pine ScriptMean reversion Machine learning Breakout MACD EMA

Author: ChaoZhang
Complexity: Starter · 57 lines
Parameters: 6
Backtest bar: 1m · intraday
Tested on: BTC/USDT · futures

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The strategy is named "Mean Reversion Reverse Strategy Based on Moving Average". The main idea is to buy when price breaks through key moving averages and take profit when reaching preset targets.

The main principle of this strategy is to capture rebound opportunities in range-bound markets by using the reversion of short-term moving averages. Specifically, when prices break through longer cycle moving averages (such as 20-day and 50-day MAs) and show signs of strong overselling, prices tend to rebound to some extent due to the mean reversion characteristic of market fluctuations. At this time, if shorter cycle moving averages (such as 10-day MA) show upward reversal signal, it would be a good timing to buy. In this strategy, it will buy when the close price is below 20-day MA while above 50-day MA, in order to capture its rebound with short-term MA reversal.

The specific entry logic is: Buy 1 lot when price breaks through 20-day MA, add 1 lot when breaking through 50-day MA, continue to add 1 lot when breaking through 100-day MA, and add up to 1 lot when breaking through 200-day MA, for a maximum of 4 lots. Take profit after reaching the preset targets. It also sets time and stop loss conditions.

Advantage Analysis

Effectively identify short-term rebound opportunities by using the reversal characteristics of moving averages
Reduce risks of single point by pyramiding orders
Lock in profits by setting take profit targets
Avoid false breakouts by using open price and previous low price filters

Risk Analysis

May face reversal risks in long holding periods. Losses would expand if market continues to fall.
MA signals may give false signals, leading to losses
May fail to fully or partially take profits if profit target is not reached

Optimization Directions

Test profitability and stability under different parameter settings
Consider combining other indicators like MACD, KD to decide entries
Choose suitable MA periods based on characteristics of different products
Introduce machine learning algorithms to dynamically optimize parameters

Summary

In general, this is a classic and universal MA trading strategy. It correctly utilizes the smoothing feature of MAs, combined with multiple MAs to identify short-term buying opportunities. It controls risks by pyramiding orders and timely profit taking. But its response to market events like significant policy news may be more passive. This is something that can be further optimized. Overall, with appropriate improvements in parameter optimization and risk control, this strategy can obtain steady excess returns.

Parameters

Name	Default	What it controls
v_input_int_1	5	Quantity 1
v_input_int_2	10	Quantity 2
v_input_int_3	15	Quantity 3
v_input_int_4	20	Quantity 4
v_input_1	true	Profit Percentage
v_input_int_5	2	Pyramiding

Source Pine Script

/*backtest
start: 2023-12-13 00:00:00
end: 2023-12-20 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("EMA_zorba1", shorttitle="zorba_ema", overlay=true)

// Input parameters
qt1 = input.int(5, title="Quantity 1", minval=1)
qt2 = input.int(10, title="Quantity 2", minval=1)
qt3 = input.int(15, title="Quantity 3", minval=1)
qt4 = input.int(20, title="Quantity 4", minval=1)
ema10 = ta.ema(close, 10)
ema20 = ta.ema(close, 20)
ema50 = ta.ema(close, 50)
ema100 = ta.ema(close, 100)
ema200 = ta.ema(close, 200)

// Date range filter
start_date = timestamp(year=2021, month=1, day=1)
end_date = timestamp(year=2024, month=10, day=27)
in_date_range = true

// Profit condition
profit_percentage = input(1, title="Profit Percentage")  // Adjust this value as needed

// Pyramiding setting
pyramiding = input.int(2, title="Pyramiding", minval=1, maxval=10)

// Buy conditions
buy_condition_1 = in_date_range and close < ema20 and close > ema50 and close < open and close < low[1]
buy_condition_2 = in_date_range and close < ema50 and close > ema100 and close < open and close < low[1]
buy_condition_3 = in_date_range and close < ema100 and close > ema200 and close < open and close < low[1]
buy_condition_4 = in_date_range and close < ema200 and close < open and close < low[1]

// Exit conditions
profit_condition = strategy.position_avg_price * (1 + profit_percentage / 100) <= close
exit_condition_1 = in_date_range and (close > ema10 and ema10 > ema20 and ema10 > ema50 and ema10 > ema100 and ema10 > ema200 and close < open) and profit_condition and close < low[1] and close < low[2]
exit_condition_2 = in_date_range and (close < ema10 and close[1] > ema10 and close < close[1] and ema10 > ema20 and ema10 > ema50 and ema10 > ema100 and ema10 > ema200 and close < open) and profit_condition and close < low[1] and close < low[2]

// Exit condition for when today's close is less than the previous day's low
//exit_condition_3 = close < low[1]

// Strategy logic
strategy.entry("Buy1", strategy.long, qty=qt1 * pyramiding, when=buy_condition_1)
strategy.entry("Buy2", strategy.long, qty=qt2 * pyramiding, when=buy_condition_2)
strategy.entry("Buy3", strategy.long, qty=qt3 * pyramiding, when=buy_condition_3)
strategy.entry("Buy4", strategy.long, qty=qt4 * pyramiding, when=buy_condition_4)

strategy.close("Buy1", when=exit_condition_1 or exit_condition_2)
strategy.close("Buy2", when=exit_condition_1 or exit_condition_2)
strategy.close("Buy3", when=exit_condition_1 or exit_condition_2)
strategy.close("Buy4", when=exit_condition_1 or exit_condition_2)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-21. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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