The BBMod strategy is a backtesting strategy that involves several conditions for entering long positions in trading. Here is a brief description of what the strategy does:
The strategy starts by populating various indicators using the provided dataframe and metadata. It calculates informative indicators based on a 1-hour timeframe and merges them with the main dataframe.
It then calculates additional indicators for the normal timeframe.
The strategy defines several conditions for entering a long position based on various indicators, including moving averages (ema), Bollinger Bands (bb), relative strength index (rsi), cumulative rsi (crsi), Elliott Wave Oscillator (ewo), NFI (Next Financial Indicator), NFIX (Next Financial Index), and Volume Weighted Average Price (vwap).
Each condition is assigned a boolean value indicating whether it is true or false. The conditions are appended to a list called "conditions." The strategy updates the "enter_tag" column of the dataframe based on the fulfilled conditions. If any conditions are met, the "enter_long" column is set to 1 to indicate the decision to enter a long position. The strategy also defines an exit trend function that sets the "exit_long" and "exit_tag" columns to 0 and "long_out," respectively, indicating the exit of a long position. The strategy has parameters for proposed leverage, maximum leverage, and trading side, but they are not utilized in the code provided. Overall, the BBMod strategy uses a combination of technical indicators and conditions to determine when to enter long positions in trading.
It then calculates additional indicators for the normal timeframe.
The strategy defines several conditions for entering a long position based on various indicators, including moving averages (ema), Bollinger Bands (bb), relative strength index (rsi), cumulative rsi (crsi), Elliott Wave Oscillator (ewo), NFI (Next Financial Indicator), NFIX (Next Financial Index), and Volume Weighted Average Price (vwap).
Each condition is assigned a boolean value indicating whether it is true or false. The conditions are appended to a list called "conditions." The strategy updates the "enter_tag" column of the dataframe based on the fulfilled conditions. If any conditions are met, the "enter_long" column is set to 1 to indicate the decision to enter a long position. The strategy also defines an exit trend function that sets the "exit_long" and "exit_tag" columns to 0 and "long_out," respectively, indicating the exit of a long position. The strategy has parameters for proposed leverage, maximum leverage, and trading side, but they are not utilized in the code provided. Overall, the BBMod strategy uses a combination of technical indicators and conditions to determine when to enter long positions in trading.
stoploss: -0.99 timeframe: 5m hash(sha256): 21a7e546c2a3c4e1fc5f44f2fd9ff9f1a51b5d9e5b3672dfd19f6fdc8391b027 indicators: bb_upperband2_40 upper EWO ha_low ema_200 ema_50 ema_13 close high_offset_2 ema_49 tail exit_long exit_tag crsi ema_200_1h vwap_high sma_21 rsi_fast bb_middleband2 volume closedelta rsi_84 ema_20 open vwap_low bb_middleband2_40 sma_15 ha_open bb_upperband2 high rsi_112 tcp_percent_4 mid bb_delta_cluc ema_24 ema_8 bb_width ema_16 ha_close sma_75 vwap cti sma_9 ema_100 ha_high lower rsi_slow sma_30 sma_28 ema_12 rsi ema_4 ema_26 low sma_20 bb_lowerband2 bb_lowerband2_40
Similar Strategies: (based on used indicators)
Strategy: BBMod_0, Similarity Score: 87.93%
Strategy: BBMod_1, Similarity Score: 87.93%
last change: 2024-11-01 15:19:09