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The NostalgiaForInfinityX2 strategy is a trading strategy implemented as a class in Python. Here is a short description of what the strategy does:
The strategy starts by populating informative indicators related to Bitcoin (BTC) based on the stake currency and timeframe specified in the configuration. It then merges the informative indicators with the main dataframe used for backtesting, dropping unnecessary columns.
Next, the strategy populates additional indicators on informative timeframes, merging them with the main dataframe and dropping unnecessary columns.
The strategy also computes indicators specifically for the base timeframe, which is set to 5 minutes.
After populating the indicators, the strategy moves on to the buy trend. It defines certain conditions for buying based on various parameters and indicator values. If the conditions are met, the corresponding buy tag is assigned to the dataframe. The sell trend is not implemented in this strategy, as the 'populate_sell_trend' function simply assigns 0 to the 'sell' column of the dataframe. The strategy includes a function for checking the time in force and current time, but its implementation is not provided in the code snippet. Lastly, the code includes functions for computing the Chaikin Money Flow (CMF) indicator and calculating the Simple Moving Average Divergence (SMADIF) indicator. Overall, the strategy focuses on populating indicators, defining buy conditions, and calculating specific technical indicators such as CMF and SMADIF.
Next, the strategy populates additional indicators on informative timeframes, merging them with the main dataframe and dropping unnecessary columns.
The strategy also computes indicators specifically for the base timeframe, which is set to 5 minutes.
After populating the indicators, the strategy moves on to the buy trend. It defines certain conditions for buying based on various parameters and indicator values. If the conditions are met, the corresponding buy tag is assigned to the dataframe. The sell trend is not implemented in this strategy, as the 'populate_sell_trend' function simply assigns 0 to the 'sell' column of the dataframe. The strategy includes a function for checking the time in force and current time, but its implementation is not provided in the code snippet. Lastly, the code includes functions for computing the Chaikin Money Flow (CMF) indicator and calculating the Simple Moving Average Divergence (SMADIF) indicator. Overall, the strategy focuses on populating indicators, defining buy conditions, and calculating specific technical indicators such as CMF and SMADIF.
stoploss: -0.99 timeframe: 5m hash(sha256): 2ae0b0200045468a890eb6a9d7d1921ce588f2b6855cdf5d5528ffd098227174 indicators: bt_agefilter_ok high open close close_s close high_s low_s btc_info_ USDTBUSDUSDCDAITUSDPAXUSDEURGBP xe5 volume live_data_ok xe1 open 5m15m1h4h1d xe4 open_s rsi_14 high low_s open_s close_s low open close 15m1h4h1d not_downtrend xe6 openclosehighlow xe2 stake_currency T3Average high_s open_s close_s runmode low xe3 date open high low close volume
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last change: 2023-06-30 12:29:54