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The ConsensusShort strategy is a trading strategy implemented in Python using the Freqtrade library. It aims to generate buy and sell signals based on a consensus score derived from various technical indicators. Here's a breakdown of the important parts of the strategy:
Timeframe: The strategy operates on 5-minute candlestick data.
ROI (Return on Investment): The minimal_roi parameter defines the desired return on investment.
In this case, it targets a 100% return within 120 candles (approximately 10 hours).
Buy Parameters: The buy_params dictionary contains parameters related to buying decisions. The buy_score_short parameter sets the minimum consensus score required for a buy signal. The leverage_num parameter determines the leverage used for trading. Sell Parameters: The sell_params dictionary contains parameters related to selling decisions. The sell_score_short parameter sets the minimum consensus score required for a sell signal. The pHSL, pPF_1, pPF_2, pSL_1, and pSL_2 parameters are used to calculate a custom stop loss based on the current profit. Custom Stop Loss: The custom_stoploss method calculates a dynamic stop loss based on the current profit. It uses the pHSL, pPF_1, pPF_2, pSL_1, and pSL_2 parameters to determine the appropriate stop loss value. Indicators: The populate_indicators method populates the DataFrame with various technical indicators calculated using the Consensus class. These indicators include RSI, Stochastic Oscillator, MACD, Hull Moving Average, VWMA, TEMA, EMA, SMA, Laguerre, Oscillator, Chaikin Money Flow, CCI, CMO, Ichimoku Cloud, Ultimate Oscillator, Williams %R, Momentum, and ADX. The consensus scores for buy and sell signals are stored in the 'consensus_buy' and 'consensus_sell' columns, respectively. Entry and Exit Trends: The populate_entry_trend and populate_exit_trend methods determine the entry and exit signals based on the consensus scores. If the 'consensus_buy' score is below the buy_score_short parameter and the volume is greater than 0, an 'enter_short' signal is generated. Similarly, if the 'consensus_sell' score is below the sell_score_short parameter and the volume is greater than 0, an 'exit_short' signal is generated. Leverage: The leverage method determines the leverage used for trading. In this case, it returns the value of the leverage_num parameter. Overall, the ConsensusShort strategy uses a combination of technical indicators and consensus scores to generate buy and sell signals. It also incorporates a custom stop loss based on the current profit and allows for leverage customization.
ROI (Return on Investment): The minimal_roi parameter defines the desired return on investment.
In this case, it targets a 100% return within 120 candles (approximately 10 hours).
Buy Parameters: The buy_params dictionary contains parameters related to buying decisions. The buy_score_short parameter sets the minimum consensus score required for a buy signal. The leverage_num parameter determines the leverage used for trading. Sell Parameters: The sell_params dictionary contains parameters related to selling decisions. The sell_score_short parameter sets the minimum consensus score required for a sell signal. The pHSL, pPF_1, pPF_2, pSL_1, and pSL_2 parameters are used to calculate a custom stop loss based on the current profit. Custom Stop Loss: The custom_stoploss method calculates a dynamic stop loss based on the current profit. It uses the pHSL, pPF_1, pPF_2, pSL_1, and pSL_2 parameters to determine the appropriate stop loss value. Indicators: The populate_indicators method populates the DataFrame with various technical indicators calculated using the Consensus class. These indicators include RSI, Stochastic Oscillator, MACD, Hull Moving Average, VWMA, TEMA, EMA, SMA, Laguerre, Oscillator, Chaikin Money Flow, CCI, CMO, Ichimoku Cloud, Ultimate Oscillator, Williams %R, Momentum, and ADX. The consensus scores for buy and sell signals are stored in the 'consensus_buy' and 'consensus_sell' columns, respectively. Entry and Exit Trends: The populate_entry_trend and populate_exit_trend methods determine the entry and exit signals based on the consensus scores. If the 'consensus_buy' score is below the buy_score_short parameter and the volume is greater than 0, an 'enter_short' signal is generated. Similarly, if the 'consensus_sell' score is below the sell_score_short parameter and the volume is greater than 0, an 'exit_short' signal is generated. Leverage: The leverage method determines the leverage used for trading. In this case, it returns the value of the leverage_num parameter. Overall, the ConsensusShort strategy uses a combination of technical indicators and consensus scores to generate buy and sell signals. It also incorporates a custom stop loss based on the current profit and allows for leverage customization.
startup_candle_count : 30 consensus_buy: -33.333% consensus_sell: -60.000%
stoploss: -0.99 timeframe: 5m hash(sha256): 5e5f8db9e5204a9eeb31aa64977ee6aa84c3ca98a20eef3bfe833c80ed25b863 indicators: sell volume consensus_buy buy consensus_sell
No similar strategies found. (based on used indicators)
last change: 2023-07-04 22:52:57