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The NostalgiaForInfinityX2 strategy is a trading strategy implemented as a class. Here is a short description of what the strategy does:
The strategy begins by populating informative indicators related to Bitcoin (BTC) using the specified stake currency or defaulting to BTC/USDT. These indicators are merged into the main dataframe for the desired timeframes (1d, 4h, 1h, 15m, 5m).
Certain columns are dropped to streamline the data.
Next, indicators on informative timeframes other than BTC are populated and merged into the dataframe for the respective timeframes.
Again, unnecessary columns are dropped. The strategy then proceeds to populate indicators specifically for the base timeframe of 5 minutes. After indicator population, the strategy moves on to defining entry trend conditions. Various logical conditions are checked, including comparisons between moving averages (sma), support and resistance levels, exponential moving averages (ema), and the relative strength index (rsi). The 'enter_tag' column is updated based on the fulfilled conditions, and an overall 'enter_long' condition is set if any conditions are met. For the exit trend, the 'exit_long' and 'exit_short' columns are initialized but not populated with any specific conditions. Finally, there is a function that checks the time in force, current time, and entry tag to determine if a buy order should proceed. The function fetches the analyzed dataframe for the specified pair and timeframe and checks if the current rate exceeds the closing rate. If the slippage is within the acceptable range, the buy order is allowed; otherwise, it is canceled. The strategy also includes helper functions for evaluating conditions based on row data and returns the result accordingly. Note: The description provided is based on the code snippet provided and may not capture the complete functionality or logic of the strategy.
Certain columns are dropped to streamline the data.
Next, indicators on informative timeframes other than BTC are populated and merged into the dataframe for the respective timeframes.
Again, unnecessary columns are dropped. The strategy then proceeds to populate indicators specifically for the base timeframe of 5 minutes. After indicator population, the strategy moves on to defining entry trend conditions. Various logical conditions are checked, including comparisons between moving averages (sma), support and resistance levels, exponential moving averages (ema), and the relative strength index (rsi). The 'enter_tag' column is updated based on the fulfilled conditions, and an overall 'enter_long' condition is set if any conditions are met. For the exit trend, the 'exit_long' and 'exit_short' columns are initialized but not populated with any specific conditions. Finally, there is a function that checks the time in force, current time, and entry tag to determine if a buy order should proceed. The function fetches the analyzed dataframe for the specified pair and timeframe and checks if the current rate exceeds the closing rate. If the slippage is within the acceptable range, the buy order is allowed; otherwise, it is canceled. The strategy also includes helper functions for evaluating conditions based on row data and returns the result accordingly. Note: The description provided is based on the code snippet provided and may not capture the complete functionality or logic of the strategy.
stoploss: -0.99 timeframe: 5m hash(sha256): 7e9e32dd8fe1f091fd6e5349da9b7cfe676d9339f2b62252bd1996ba04a74840 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: 2024-04-01 03:58:48