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The CofiBitStrategy is a trading strategy implemented in Python for backtesting purposes. It utilizes technical analysis indicators to generate buy and sell signals for trading. Here's a breakdown of what the strategy does:
It sets a predefined minimal ROI (Return on Investment) for the strategy.
The ROI values are specified for different percentage gains (40%, 30%, 20%, and 0%).
These values determine the profit targets for the trades.
It defines an optimal stop loss value, which is the percentage at which a trade will be exited if the price moves against the expected direction. In this case, the stop loss is set at -0.25 (25% loss). The strategy operates on a timeframe of 15 minutes, meaning it analyzes price data within 15-minute intervals. The populate_indicators function calculates several technical indicators using the price data. These indicators include: Stochastic Fast (STOCHF) indicator with parameters (5, 3, 0, 3, 0), which generates the 'fastd' and 'fastk' values. Exponential Moving Averages (EMA) of the high, close, and low prices with a time period of 5. Additionally, the Average Directional Index (ADX) is calculated using the price data. The populate_buy_trend function determines the conditions for generating a buy signal. The buy signal is triggered when the following conditions are met: The opening price is below the EMA low. The 'fastk' line crosses above the 'fastd' line. The 'fastk' and 'fastd' values are both below 30. The ADX value is above 30. When these conditions are satisfied, a 'buy' column is set to 1 in the dataframe. The populate_sell_trend function determines the conditions for generating a sell signal. The sell signal is triggered when any of the following conditions are met: The opening price is equal to or above the EMA high. Either the 'fastk' or 'fastd' line crosses above 70. When these conditions are satisfied, a 'sell' column is set to 1 in the dataframe. Overall, the CofiBitStrategy uses a combination of moving averages, stochastic indicators, and the ADX to identify potential buying and selling opportunities in the market.
The ROI values are specified for different percentage gains (40%, 30%, 20%, and 0%).
These values determine the profit targets for the trades.
It defines an optimal stop loss value, which is the percentage at which a trade will be exited if the price moves against the expected direction. In this case, the stop loss is set at -0.25 (25% loss). The strategy operates on a timeframe of 15 minutes, meaning it analyzes price data within 15-minute intervals. The populate_indicators function calculates several technical indicators using the price data. These indicators include: Stochastic Fast (STOCHF) indicator with parameters (5, 3, 0, 3, 0), which generates the 'fastd' and 'fastk' values. Exponential Moving Averages (EMA) of the high, close, and low prices with a time period of 5. Additionally, the Average Directional Index (ADX) is calculated using the price data. The populate_buy_trend function determines the conditions for generating a buy signal. The buy signal is triggered when the following conditions are met: The opening price is below the EMA low. The 'fastk' line crosses above the 'fastd' line. The 'fastk' and 'fastd' values are both below 30. The ADX value is above 30. When these conditions are satisfied, a 'buy' column is set to 1 in the dataframe. The populate_sell_trend function determines the conditions for generating a sell signal. The sell signal is triggered when any of the following conditions are met: The opening price is equal to or above the EMA high. Either the 'fastk' or 'fastd' line crosses above 70. When these conditions are satisfied, a 'sell' column is set to 1 in the dataframe. Overall, the CofiBitStrategy uses a combination of moving averages, stochastic indicators, and the ADX to identify potential buying and selling opportunities in the market.
stoploss: -0.25 timeframe: 15m hash(sha256): 829d5097d48a532aac8a187844ee2a6828dafe8ecc8c5e75f0eba4c063c3e07d indicators: ema_close adx ema_high open ema_low fastd fastk
No similar strategies found. (based on used indicators)
last change: 2023-07-04 00:52:50