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Strategy: TEMABounce_2
Downloaded: 20230102
Stoploss: 0
The TEMABounce strategy is a trading strategy that utilizes various technical analysis indicators to generate buy and sell signals. Here is a brief description of what the strategy does: The populate_indicators function adds the following indicators to the given DataFrame: RSI (Relative Strength Index) MACD (Moving Average Convergence Divergence) and its components (macd, macdsignal, macdhist) Bollinger Bands and related metrics (bb_lowerband, bb_middleband, bb_upperband, bb_percent, bb_width, bb_gain) TEMA (Triple Exponential Moving Average) and its related metrics (tema, tema_short, tema_angle, tema_diff) SMA (Simple Moving Average) SAR (Stop and Reverse) The populate_entry_trend function determines the buy signals based on the populated indicators. The conditions for a buy signal include: Volume greater than 0 MACD histogram greater than or equal to 0 (if buy_macd_enabled is True) TEMA difference greater than 0 Bollinger Bands gain greater than or equal to the specified threshold (if buy_bb_enabled is True) Crossing above 0 for the TEMA angle The populate_exit_trend function determines the sell signals based on the populated indicators.

The conditions for a sell signal include: Crossing below 0 for the TEMA angle Close price greater than the TEMA TEMA difference greater than or equal to the negative of the specified threshold (sell_diff) If sell_hold is True, it sets the sell signal to 0 for all data points (no selling) The strategy aims to identify potential buying opportunities when the indicators align with the specified conditions and sell positions when the conditions for selling are met.


stoploss: 0
timeframe: 5m
hash(sha256): 0e2b74b766d5dbfc82587e1e561445de6d57a8f118704393f551fbf3940098da
indicators:
upper tema close bb_gain bb_lowerband
bb_percent macdhist tema_diff sma volume
sar tema_angle macdsignal mid bb_width
macd lower tema_dif tema_short bb_middleband
rsi bb_upperband

Similar Strategies: (based on used indicators)

Strategy: TEMABounce, Similarity Score: 95.65%

last change: 2024-07-27 04:29:15