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The "conny" strategy is a backtesting strategy that uses various technical indicators to determine buy and sell signals for trading. It implements the IStrategy interface from the freqtrade.strategy.interface module. Here's a breakdown of the important parts of the strategy:
The strategy uses the following indicators from the talib and qtpylib libraries: RSI, Stochastic, MACD, Hull Moving Average (Hull), Volume Weighted Moving Average (VWMA), Triple Exponential Moving Average (TEMA), Exponential Moving Average (EMA), Simple Moving Average (SMA), Laguerre, Oscillator (OSC), Chaikin Money Flow (CMF), Commodity Channel Index (CCI), Chande Momentum Oscillator (CMO), Ichimoku Cloud (Ichimoku), Ultimate Oscillator (Ultimate Oscillator), and Williams %R (Williams).
These indicators are evaluated using the Consensus class from the technical.consensus module.
The minimal_roi dictionary defines the minimum return on investment (ROI) thresholds for different time periods.
For example, if the holding period is 0 to 10 minutes, the minimum ROI required is 0.025 (2.5%). The stoploss variable sets the stop-loss threshold for exiting a trade. If the price drops by 2.03%, the strategy will sell the asset. The timeframe variable specifies the timeframe for the candlestick data used in the strategy. In this case, it's set to '15m', indicating 15-minute candles. The process_only_new_candles variable is set to True, indicating that the strategy should only process new candles that appear after the strategy starts running. The use_sell_signal variable is set to True, indicating that the strategy should use a sell signal to trigger a sell order. The sell_profit_only variable is set to False, indicating that the strategy should sell even if it results in a loss. The ignore_roi_if_buy_signal variable is set to True, indicating that the ROI thresholds defined in minimal_roi will be ignored if a buy signal is present. The startup_candle_count variable is set to 30, specifying the number of candles required before the strategy starts generating buy/sell signals. The informative_pairs function returns an empty list, indicating that the strategy does not require additional informative pairs. The populate_indicators function populates the dataframe with the evaluated indicator values. It calls various methods of the Consensus class to evaluate different indicators and assigns the scores to 'consensus_buy' and 'consensus_sell' columns in the dataframe. The populate_buy_trend function populates the 'buy' column in the dataframe based on the buy conditions. If the 'consensus_buy' score is greater than 45 and the volume is greater than 0, a buy signal is generated. The populate_sell_trend function populates the 'sell' column in the dataframe based on the sell conditions. If the 'consensus_sell' score is greater than 88 and the volume is greater than 0, a sell signal is generated. Overall, the strategy evaluates multiple technical indicators using a consensus approach and generates buy and sell signals based on predefined criteria.
These indicators are evaluated using the Consensus class from the technical.consensus module.
The minimal_roi dictionary defines the minimum return on investment (ROI) thresholds for different time periods.
For example, if the holding period is 0 to 10 minutes, the minimum ROI required is 0.025 (2.5%). The stoploss variable sets the stop-loss threshold for exiting a trade. If the price drops by 2.03%, the strategy will sell the asset. The timeframe variable specifies the timeframe for the candlestick data used in the strategy. In this case, it's set to '15m', indicating 15-minute candles. The process_only_new_candles variable is set to True, indicating that the strategy should only process new candles that appear after the strategy starts running. The use_sell_signal variable is set to True, indicating that the strategy should use a sell signal to trigger a sell order. The sell_profit_only variable is set to False, indicating that the strategy should sell even if it results in a loss. The ignore_roi_if_buy_signal variable is set to True, indicating that the ROI thresholds defined in minimal_roi will be ignored if a buy signal is present. The startup_candle_count variable is set to 30, specifying the number of candles required before the strategy starts generating buy/sell signals. The informative_pairs function returns an empty list, indicating that the strategy does not require additional informative pairs. The populate_indicators function populates the dataframe with the evaluated indicator values. It calls various methods of the Consensus class to evaluate different indicators and assigns the scores to 'consensus_buy' and 'consensus_sell' columns in the dataframe. The populate_buy_trend function populates the 'buy' column in the dataframe based on the buy conditions. If the 'consensus_buy' score is greater than 45 and the volume is greater than 0, a buy signal is generated. The populate_sell_trend function populates the 'sell' column in the dataframe based on the sell conditions. If the 'consensus_sell' score is greater than 88 and the volume is greater than 0, a sell signal is generated. Overall, the strategy evaluates multiple technical indicators using a consensus approach and generates buy and sell signals based on predefined criteria.
stoploss: -0.02 timeframe: 15m hash(sha256): a9c7ea0fbb6cf41aa2fbebc2ae1719b44d36ade67397adc026b31ca5cbf241e5 indicators: sell volume consensus_buy buy consensus_sell
No similar strategies found. (based on used indicators)
last change: 2023-07-05 09:36:06