This strategy is a momentum strategy based on moving average lines

Author: ChaoZhang
Complexity: Intermediate · 58 lines
Parameters: 9
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

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Overview

This strategy is a momentum strategy based on moving average lines. It generates trading signals by calculating simple moving averages of different periods and comparing their crossover situations. Specifically, when the short-term moving average line crosses above the long-term moving average line, a buy signal is generated; when the short-term moving average line crosses below the long-term moving average line, a sell signal is generated.

Strategy Principle

The core logic of this strategy is based on the momentum effect, which is the persistence of stock price trends. Moving average lines can effectively reflect the change trend of stock prices. When the short-term moving average line crosses above the long-term moving average line, it means that the stock price starts to enter an upward trend; on the contrary, when the short-term moving average line crosses below the long-term moving average line, it means that the stock price starts to enter a downward trend. This strategy generates trading signals based on this principle.

Specifically, a 13-day simple moving average and a 34-day simple moving average are defined in the strategy. After calculating these two moving averages based on the daily closing price, their magnitude relationship is compared. If the 13-day line crosses above the 34-day line, a buy signal is generated, indicating that the stock price enters an upward trend and a long position should be established; if the 13-day line crosses below the 34-day line, a sell signal is generated, indicating that the stock price enters a downward trend and the position should be closed out.

Advantage Analysis

The biggest advantage of this strategy is that it is simple and easy to understand and implement. The moving average line is one of the most basic and commonly used technical indicators. Its principle is simple and easy to understand and apply. At the same time, the moving average line crossover signal has also been proven effective through long-term practice.

In addition, the parameter setting of this strategy is flexible and can be adjusted according to different varieties and market conditions. For example, the cycle parameters of the moving average line can be changed to adjust the sensitivity of the strategy. This provides room for strategy optimization and adjustment.

Risk Analysis

The biggest risk of this strategy is that there may be more false signals and getting caught in range-bound markets. When prices fluctuate sharply, moving average lines may produce frequent crossovers, resulting in false signals. At this point, you need to adjust the cycle parameters of the moving average line to filter out some noise.

In addition, when there is a larger market reversal, the stop loss point of the strategy may be broken through, resulting in larger losses. This requires optimizing the stop loss strategy and appropriately relaxing the stop loss range.

Optimization Directions

The following aspects of this strategy can be optimized:

Optimize the cycle parameters of the moving average line to find the optimal combination of parameters for different varieties and market conditions.
Add filtration of other technical indicators such as MACD and KD to avoid generating false signals in range-bound markets.
Optimize and dynamically adjust the stop loss strategy to avoid the stop loss point being too close while ensuring the stop loss, with a higher probability of being broken through.
Increase position management mechanisms such as fixed investment and position ratio to control single transaction risk.

Conclusion

This strategy is a very classic moving average crossover strategy. It generates buy and sell signals by calculating and comparing the relationship between short-term and long-term moving averages. The advantages of this strategy are simple, flexible parameters, and suitable for beginners to learn; the disadvantages are that the signals may not be stable enough and it is easy to get caught in range-bound markets. With proper optimization, it can still become a very practical quantitative strategy.

Parameters

Name	Default	What it controls
v_input_1	false	Enable short entries?
v_input_2	5	From Month
v_input_3	18	From Day
v_input_4	2018	From Year
v_input_5	9	To Month
v_input_6	true	To Day
v_input_7	2018	To Year
v_input_8	10	Stop Loss %
v_input_9	30	Take Profit %

Source Pine Script

/*backtest
start: 2023-01-16 00:00:00
end: 2024-01-22 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
// TODO: update strategy name
strategy("{STRATEGY NAME}", overlay=true)

// === TA LOGIC ===

//
//
// TODO: PUT YOUR TA LOGIC HERE
LONG_SIGNAL_BOOLEAN = crossover(sma(close, 13), sma(close, 34))
SHORT_SIGNAL_BOOLEAN = crossunder(sma(close, 12), sma(close, 21))

// === INPUT BACKTEST DATE RANGE ===
enableShorts = input(false, title="Enable short entries?")

FromMonth = input(defval = 5, title = "From Month", minval = 1, maxval = 12)
FromDay   = input(defval = 18, title = "From Day", minval = 1, maxval = 31)
FromYear  = input(defval = 2018, title = "From Year", minval = 2017)
ToMonth   = input(defval = 9, title = "To Month", minval = 1, maxval = 12)
ToDay     = input(defval = 1, title = "To Day", minval = 1, maxval = 31)
ToYear    = input(defval = 2018, title = "To Year", minval = 2017)

start     = timestamp(FromYear, FromMonth, FromDay, 00, 00)  // backtest start window
finish    = timestamp(ToYear, ToMonth, ToDay, 23, 59)        // backtest finish window
window()  => true // create function "within window of time"

// === STRATEGY BUY / SELL ENTRIES ===

// TODO: update the placeholder LONG_SIGNAL_BOOLEAN and SHORT_SIGNAL_BOOLEAN to signal
// long and short entries
buy() => window() and LONG_SIGNAL_BOOLEAN
sell() => window() and SHORT_SIGNAL_BOOLEAN

if buy()
    strategy.entry("Long", strategy.long, comment="Long")

if sell()
    if (enableShorts)
        strategy.entry("Short", strategy.short, comment="Short")
    else
        strategy.close("Long")

// === BACKTESTING: EXIT strategy ===
sl_inp = input(10, title='Stop Loss %', type=float)/100
tp_inp = input(30, title='Take Profit %', type=float)/100

stop_level = strategy.position_avg_price * (1 - sl_inp)
take_level = strategy.position_avg_price * (1 + tp_inp)

strategy.exit("Stop Loss/Profit", "Long", stop=stop_level, limit=take_level)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-23. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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