The moving average crossover trading strategy is a relatively common quantitative trading strategy. This strategy generates trading signals by calculating moving averages of different periods and according to their crossover situations. Specifically, it calculates the exponential moving averages (EMA) of 4 periods, 8 periods and 20 periods. When the short-term EMA crosses above the long-term EMA, go long; when the short-term EMA crosses below the long-term EMA, go short.
The core logic of this strategy is:
Through this method, we take advantage of the crossover between different period moving averages to judge market signals, and use the direction of the longest period moving average to filter false signals, constructing a stable trading strategy.
The main advantages of this strategy are:
There are also some risks with this strategy:
The main solutions are:
The strategy can be optimized in the following aspects:
Period optimization: Determine the optimal MA period combination according to different varieties.
Stop loss optimization: Reasonably set stop loss points to control single loss.
Parameter optimization: Dynamically optimize parameters using genetic algorithms, Markov chains, etc.
Model fusion: Integrate with LSTM, RNN and other deep learning models to extract more Alpha.
Portfolio optimization: Combine with other technical indicator strategies to construct strategy portfolios.
In general, the moving average crossover strategy is a relatively classic and commonly used quantitative trading strategy. This strategy has simple logic and is easy to understand and implement, with certain stability. But there are also some problems, such as generating false signals, inability to adapt to market changes, etc. These issues can be improved through parameter optimization, stop loss optimization, model fusion, and other methods. Overall, the moving average strategy can be used as a basic module in the strategy toolbox, combined with more complex strategies to build robust complex strategies.
/*backtest start: 2023-12-01 00:00:00 end: 2023-12-31 23:59:59 period: 1h basePeriod: 15m exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=3 //future strategy //strategy(title = "stub", default_qty_type = strategy.fixed, default_qty_value = 1, overlay = true, commission_type=strategy.commission.cash_per_contract,commission_value=2.05) //stock strategy strategy(title = "stub", overlay = true) //forex strategy //strategy(title = "stub", default_qty_type = strategy.percent_of_equity, default_qty_value = 100, overlay = true) //crypto strategy //strategy(title = "stub", default_qty_type = strategy.percent_of_equity, default_qty_value = 100, overlay = true, commission_type=strategy.commission.percent,commission_value=.0,default_qty_value=10000) testStartYear = input(1900, "Backtest Start Year") testStartMonth = input(1, "Backtest Start Month") testStartDay = input(1, "Backtest Start Day") testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,0,0) testEndYear = input(2018, "Backtest Start Year") testEndMonth = input(12, "Backtest Start Month") testEndDay = input(1, "Backtest Start Day") testPeriodEnd = timestamp(testStartYear,testStartMonth,testStartDay,0,0) testPeriod() => true ema1 = ema(close,4) ema2 = ema(close,8) ema3 = ema(close,20) go_long = ema1[0] > ema2[0] and ema3[0] > ema3[1] exit_long = ema1[0] < ema2[0] or ema3[0] < ema3[1] go_short = ema1[0] < ema2[0] and ema3[0] < ema3[1] exit_short = ema1[0] > ema2[0] or ema3[0] > ema3[1] if testPeriod() strategy.entry("simpleBuy", strategy.long, when=go_long) strategy.exit("simpleBuy", "simpleSell",when=exit_long) strategy.entry("simpleSell", strategy.short,when=go_short) strategy.exit("simpleSell", "simpleSell",when=exit_short)template: strategy.tpl:40:21: executing "strategy.tpl" at <.api.GetStrategyListByName>: wrong number of args for GetStrategyListByName: want 7 got 6