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The NostalgiaForInfinityX2 strategy is a trading strategy that involves backtesting various indicators and conditions to determine entry and exit points for trading. Here is a breakdown of what the strategy does:
populate_indicators: This function populates informative indicators for Bitcoin (BTC) on different timeframes. It retrieves the necessary data and merges it with the main dataframe.
It also removes unnecessary columns.
populate_entry_trend: This function determines the entry conditions for a trade.
It checks various indicators and conditions, such as moving averages (sma), support and resistance levels, exponential moving averages (ema), relative strength index (rsi), and volume. If the conditions are met, the strategy sets an "enter_tag" and "enter_long" for the corresponding trade. populate_exit_trend: This function sets the exit conditions for a trade. It initializes the "exit_long" and "exit_short" columns in the dataframe. time_in_force: This function checks the time in force for a trade and calculates the slippage. If the slippage is within a certain threshold, the trade is considered valid; otherwise, it is canceled. Additional functions: There are additional functions in the code that are not explicitly mentioned but likely perform supporting tasks such as data retrieval, manipulation, and analysis. Overall, the strategy combines multiple indicators and conditions to determine favorable entry and exit points for trading. It utilizes indicators such as moving averages, support and resistance levels, exponential moving averages, and relative strength index to make trading decisions. The strategy aims to optimize trading performance through backtesting and analysis.
It also removes unnecessary columns.
populate_entry_trend: This function determines the entry conditions for a trade.
It checks various indicators and conditions, such as moving averages (sma), support and resistance levels, exponential moving averages (ema), relative strength index (rsi), and volume. If the conditions are met, the strategy sets an "enter_tag" and "enter_long" for the corresponding trade. populate_exit_trend: This function sets the exit conditions for a trade. It initializes the "exit_long" and "exit_short" columns in the dataframe. time_in_force: This function checks the time in force for a trade and calculates the slippage. If the slippage is within a certain threshold, the trade is considered valid; otherwise, it is canceled. Additional functions: There are additional functions in the code that are not explicitly mentioned but likely perform supporting tasks such as data retrieval, manipulation, and analysis. Overall, the strategy combines multiple indicators and conditions to determine favorable entry and exit points for trading. It utilizes indicators such as moving averages, support and resistance levels, exponential moving averages, and relative strength index to make trading decisions. The strategy aims to optimize trading performance through backtesting and analysis.
stoploss: -0.99 timeframe: 5m hash(sha256): 734b58c571652fc76c2c72bae518dae4fea434946d91ef7fea02395249243502 indicators: sma_200_1h upper close res1 low_s pct_close_max_24 volume sup_level_1h xe1 sma_50 rsi_14 high r_480 crossed_below_ema_12_26 xe6 openclosehighlow bb20_2_mid ema_12 T3Average ema_26 runmode res_level rsi_14_4h close_max_48 pct_close_max_72 pivot USDTBUSDUSDCDAITUSDPAXUSDEURGBP bb20_2_upp_1h sma_50_1h live_data_ok 5m15m1h4h1d bb20_2_low sma_50_4h sup3_1d ema_200_pct_change_144 lower stake_currency high_s open_s close_s sup_level xe3 sma_100 date open high low close volume bt_agefilter_ok high open
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last change: 2022-11-06 00:09:11