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The NostalgiaForInfinityNext strategy is a backtesting strategy implemented in Python. It performs several tasks to populate indicators, determine buy signals, and make buying decisions. Here's a breakdown of what the strategy does:
Populating Indicators:
It fetches historical price data for BTC and calculates various indicators.
It merges the BTC indicators with the main dataframe.
It removes unnecessary columns from the dataframe.
It repeats the above steps for informative timeframes and resampled timeframes if specified. Populating Buy Trend: It defines a list of conditions for buying. It assigns an empty string to the 'buy_tag' column in the dataframe. It iterates over buy protection parameters and checks if buy conditions are enabled. It adds specific conditions based on different indicators and parameters. It checks additional conditions related to safe dips, safe pump, BTC not in a downtrend, and close price relative to pivot points. It adds an age filter condition if not in live or dry run mode. The strategy performs these tasks to generate buy signals based on the defined conditions. The details of the specific indicators, parameters, and conditions can be found in the code provided.
It merges the BTC indicators with the main dataframe.
It removes unnecessary columns from the dataframe.
It repeats the above steps for informative timeframes and resampled timeframes if specified. Populating Buy Trend: It defines a list of conditions for buying. It assigns an empty string to the 'buy_tag' column in the dataframe. It iterates over buy protection parameters and checks if buy conditions are enabled. It adds specific conditions based on different indicators and parameters. It checks additional conditions related to safe dips, safe pump, BTC not in a downtrend, and close price relative to pivot points. It adds an age filter condition if not in live or dry run mode. The strategy performs these tasks to generate buy signals based on the defined conditions. The details of the specific indicators, parameters, and conditions can be found in the code provided.
stoploss: -0.5 timeframe: 5m hash(sha256): a824dd08d6cff96df2031c75628e907006cea5d286d9da2bc50c50551cebebf4 indicators: sma_200_1h upper safe_pump_type safe_pump_24_30 leading_senkou_span_b_1h close ema_15 res1 senkou_a sell_pump_36_2 sell_pump_24_1_1h sma_5 low_s safe_pump_period ema_25 ema_200_1h tenkan_sen 39 sma200_rising_val r_480_1h safe_pump_24_100 volume rsi_4 sma_200_dec_20 leading_senkou_span_a f"global_buy_protection_paramsclose_under_pivot_type_1d senkou_a senkou_b safe_pump_24_120 tg_dataframe sell_pump_36_1_1h ema_20 source safe_pump_48_30 momdiv_coh oc_pct_change_48 rsi_14 sell_pump_48_1_1h high to
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last change: 2022-07-02 19:54:08