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Strategy vault / Breakout

End of Month Breakout on 200 Day Moving Average Strategy

This strategy is based on the price breakout of the 200-day moving average at month end to capture the trend direction of stock prices. A long position will be established when the price breaks through the 200-day…

Pine ScriptBreakoutMean reversionTrend followingSMABollinger Bands
Author
ChaoZhang
Complexity
Starter · 45 lines
Parameters
6
Backtest bar
1d · swing
Tested on
BTC/USDT · futures
Jump to codeView in vaultOriginal on FMZ

IMG

Overview

This strategy is based on the price breakout of the 200-day moving average at month end to capture the trend direction of stock prices. A long position will be established when the price breaks through the 200-day MA, otherwise the position will be cleared.

Strategy Principle

  1. Use the 200-day simple moving average dma200 as an indicator to judge the price trend
  2. On the last trading day of each month, judge whether the closing price close is higher than dma200
  3. If the closing price breaks through the 200-day MA, establish a full long position at the opening of next trading day
  4. If the closing price breaks below the 200-day MA, clear all positions at the opening of next trading day
  5. This can achieve the effect of trend following, establishing positions when stock prices enter an upward trend and avoiding downward trends

Advantage Analysis

  1. The advantage of the strategy is that it is simple and effective, easy to understand and implement
  2. Taking positions at month end can reduce trading frequency and minimize trading costs and slippage impacts
  3. The 200-day MA is a very commonly used medium and long term trend judgment indicator, effective for most stocks
  4. The strategy has relatively small drawdown and maximum decline, with controllable risks

Risk Analysis

  1. The 200-day MA may not be sensitive enough for some stocks to capture price reversals in time
  2. There is only 1 trading point per month for taking positions, which may miss upside/downside opportunities
  3. The strategy may fail to judge correctly when the overall market trend is uncertain
  4. Other indicators should be combined to reduce these risks

Optimization Directions

  1. Consider increasing trading points at beginning or middle of month to improve strategy frequency
  2. Add indicators like Bollinger Bands to judge price fluctuation and avoid wrong trades
  3. Evaluate fitting effects of different MA parameters on different stocks to find optimal parameter combinations
  4. Establish dynamic position sizing mechanisms to stop loss actively when drawdown gets too high

Summary

The strategy is relatively simple and practical overall, effectively capturing medium and long term price trends of stocks through month end breakout of the 200-day MA, with relatively small drawdown and risks. By combining more indicators and dynamic optimizations, the stability and profitability of the strategy can be further enhanced.

Parameters

NameDefaultWhat it controls
v_input_int_1trueFrom Day
v_input_int_2trueFrom Month
v_input_int_32018From Year
v_input_int_4trueTo Day
v_input_int_5trueTo Month
v_input_int_69999To Year

Source Pine Script

/*backtest
start: 2022-12-01 00:00:00
end: 2023-12-07 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © muscleriot
//200 dma
//2000-2016 backtested 
//1 signal per month only at end of month
//If > 200DMA enter long
//If < 200DMA goto cash
//results: 318% drawdown 17% vs 125% with 55% drawdown for buy and hold
//@version=5
strategy("200DMA last DOM - ajh", overlay =true,default_qty_type=strategy.percent_of_equity, default_qty_value=100)
// Use 100% of  equity always

dma200 = ta.sma(close, 200)
plot(dma200, color=color.red, linewidth = 2)
//e =dayofmonth(time)
// backtesting date range
from_day = input.int(defval=1, title="From Day", minval=1, maxval=31)
from_month = input.int(defval=1, title="From Month", minval=1, maxval=12)
from_year = input.int(defval=2018, title="From Year", minval=1900)

to_day = input.int(defval=1, title="To Day", minval=1, maxval=31)
to_month = input.int(defval=1, title="To Month", minval=1, maxval=12)
to_year = input.int(defval=9999, title="To Year", minval=1900)

time_cond = time > timestamp(from_year, from_month, from_day, 00, 00) and 
   time < timestamp(to_year, to_month, to_day, 23, 59)



xLong = dayofmonth(time) == 30 and (close > dma200) ? true : na
xSell = dayofmonth(time) == 30 and (close < dma200) ? true : na
plotchar(xLong, "long","L", color=color.green)    
plotchar(xSell, "Sell","S", color=color.red)    
if (xLong == true) and time_cond
    strategy.entry("long", strategy.long)
if (xSell == true) and time_cond
    strategy.close("long")

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-08. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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