This strategy uses dynamic timeframe high-low breakouts to generate trading signals. It determines whether to buy or sell by comparing the highest and lowest prices of the current timeframe with the closing price of the previous timeframe plus or minus a certain number of points. This approach can adapt to different market trends and volatility, thus improving the adaptability and flexibility of the strategy.
The core of this strategy is to use the high and low points of different timeframes to determine price trends. First, it obtains the highest price, lowest price, and closing price data corresponding to the user-selected timeframe. Then, it determines the buy signal by comparing whether the highest price of the current timeframe is greater than the closing price of the previous timeframe plus a certain number of points. Similarly, it determines the sell signal by comparing whether the lowest price of the current timeframe is less than the closing price of the previous timeframe minus a certain number of points. Once a buy or sell signal appears, the strategy will open or close positions accordingly. In addition, the strategy will mark the buy and sell signals on the chart and plot the equity curve of the strategy for intuitive evaluation of the strategy performance.
The dynamic timeframe high-low breakout strategy generates trading signals based on price breakouts of high and low points in different timeframes. The strategy logic is clear, adaptable, and easy to implement and optimize. However, it also has problems such as parameter sensitivity, overfitting, and market risk, which need to be continuously optimized and improved in actual application. By dynamically adjusting parameters, introducing risk management, combining with other indicators, and optimizing code efficiency, the robustness and profitability of the strategy can be further improved, providing effective tools and ideas for quantitative trading.
/*backtest start: 2023-05-28 00:00:00 end: 2024-06-02 00:00:00 period: 1d basePeriod: 1h exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=5 strategy(" NIFTY 65-15 ", overlay=true) // Define input options for point settings and timeframe points = input.int(60, title="Point Threshold", minval=1, step=1) timeframe = input.timeframe("60", title="Timeframe", options=["1", "3", "5", "15", "30", "60", "240", "D", "W", "M"]) // Calculate high and low of the selected timeframe high_timeframe = request.security(syminfo.tickerid, timeframe, high) low_timeframe = request.security(syminfo.tickerid, timeframe, low) close_timeframe = request.security(syminfo.tickerid, timeframe, close) // Define conditions for Buy and Sell buyCondition = high_timeframe > (close_timeframe[1] + points) sellCondition = low_timeframe < (close_timeframe[1] - points) // Entry and exit rules if (buyCondition) strategy.entry("Buy", strategy.long) if (sellCondition) strategy.entry("Sell", strategy.short) // Close the positions based on the conditions if (sellCondition) strategy.close("Buy") if (buyCondition) strategy.close("Sell") // Plot Buy and Sell signals on the chart plotshape(series=buyCondition, title="Buy Entry", color=color.green, style=shape.triangleup, location=location.belowbar) plotshape(series=sellCondition, title="Sell Entry", color=color.red, style=shape.triangledown, location=location.abovebar) // Plot the equity curve of the strategy plot(strategy.equity, title="Equity", color=color.blue, linewidth=2)template: strategy.tpl:40:21: executing "strategy.tpl" at <.api.GetStrategyListByName>: wrong number of args for GetStrategyListByName: want 7 got 6