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The "TheForce" strategy is designed to backtest trading strategies using various technical indicators. The strategy has three main components: indicator population, buy signal population, and sell signal population. In the "populate_indicators" function, the strategy calculates and adds several technical indicators to the input DataFrame.
These indicators include STOCHF (Stochastic Fast), STOCHRSI (Stochastic RSI), and MACD (Moving Average Convergence Divergence).
Additionally, it computes the exponential moving averages (EMA) for the closing and opening prices.
The "populate_buy_trend" function populates the buy signal for the DataFrame based on the values of the calculated indicators. It sets the "buy" column to 1 for rows that satisfy specific conditions. These conditions include the fast %K and %D values being between 20 and 80, the MACD being greater than its previous value, the MACD signal being greater than its previous value, the closing price being greater than its previous value, and the EMA of the closing price being greater than or equal to the EMA of the opening price. Similarly, the "populate_sell_trend" function populates the sell signal for the DataFrame based on the indicator values. It sets the "sell" column to 1 for rows that meet certain conditions. These conditions include the fast %K and %D values being less than or equal to 80, the MACD being less than its previous value, the MACD signal being less than its previous value, and the EMA of the closing price being less than the EMA of the opening price. Overall, the strategy aims to identify buying opportunities when the indicators align with specific conditions and selling opportunities when the indicators suggest a potential downturn.
These indicators include STOCHF (Stochastic Fast), STOCHRSI (Stochastic RSI), and MACD (Moving Average Convergence Divergence).
Additionally, it computes the exponential moving averages (EMA) for the closing and opening prices.
The "populate_buy_trend" function populates the buy signal for the DataFrame based on the values of the calculated indicators. It sets the "buy" column to 1 for rows that satisfy specific conditions. These conditions include the fast %K and %D values being between 20 and 80, the MACD being greater than its previous value, the MACD signal being greater than its previous value, the closing price being greater than its previous value, and the EMA of the closing price being greater than or equal to the EMA of the opening price. Similarly, the "populate_sell_trend" function populates the sell signal for the DataFrame based on the indicator values. It sets the "sell" column to 1 for rows that meet certain conditions. These conditions include the fast %K and %D values being less than or equal to 80, the MACD being less than its previous value, the MACD signal being less than its previous value, and the EMA of the closing price being less than the EMA of the opening price. Overall, the strategy aims to identify buying opportunities when the indicators align with specific conditions and selling opportunities when the indicators suggest a potential downturn.
Biased Indicators
macdsignal, macdhist
stoploss: -0.015 timeframe: 15m hash(sha256): dc3537c5cbe9d1d80e0db442a4ceef2fcd0ba7ccf40bd637515ec20a3f661485 indicators: ema5o macdsignal fastk_rsi close fastd macd open macdhist fastd_rsi ema5c fastk
No similar strategies found. (based on used indicators)
last change: 2023-07-05 11:29:18