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The given code represents a strategy called "Inverse" for backtesting trading strategies. Here is a brief description of what the strategy does:
The populate_indicators function is responsible for generating indicators for both the informative timeframe and the normal timeframe. It merges the informative indicators with the normal timeframe data and removes unnecessary columns.
The populate_buy_trend function identifies buy signals based on a set of conditions.
These conditions involve comparing different indicators, such as the Fisher CCI (Commodity Channel Index), SSL (Simplified Supertrend Line), EMAs (Exponential Moving Averages), and volume.
If all the conditions are met, the buy column in the dataframe is set to 1 to indicate a buy signal. The populate_sell_trend function identifies sell signals based on the Fisher CCI and volume conditions. If the conditions are met, the sell column in the dataframe is set to 1 to indicate a sell signal. The code after the function definitions calculates additional indicators for the dataframe, including ATR (Average True Range), smaHigh, smaLow, hlv (high/low value), sslDown, and sslUp. These indicators are derived from the high, low, and close prices. The final output of the strategy is the sslDown and sslUp values. In summary, the Inverse strategy uses various indicators to generate buy and sell signals based on specific conditions. It also calculates additional indicators related to price volatility and trend lines. The strategy aims to identify potential trading opportunities by analyzing the market data.
The populate_buy_trend function identifies buy signals based on a set of conditions.
These conditions involve comparing different indicators, such as the Fisher CCI (Commodity Channel Index), SSL (Simplified Supertrend Line), EMAs (Exponential Moving Averages), and volume.
If all the conditions are met, the buy column in the dataframe is set to 1 to indicate a buy signal. The populate_sell_trend function identifies sell signals based on the Fisher CCI and volume conditions. If the conditions are met, the sell column in the dataframe is set to 1 to indicate a sell signal. The code after the function definitions calculates additional indicators for the dataframe, including ATR (Average True Range), smaHigh, smaLow, hlv (high/low value), sslDown, and sslUp. These indicators are derived from the high, low, and close prices. The final output of the strategy is the sslDown and sslUp values. In summary, the Inverse strategy uses various indicators to generate buy and sell signals based on specific conditions. It also calculates additional indicators related to price volatility and trend lines. The strategy aims to identify potential trading opportunities by analyzing the market data.
stoploss: -0.2 timeframe: 1h hash(sha256): c4ff9f2216fa8abfce58d782bdce8e9645489d796f23c255096dbf0a02f7c712
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-29 08:12:54