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The "NostalgiaForInfinityX2" strategy is a trading strategy that involves backtesting multiple trading strategies. Here's a short description of what the strategy does:
The strategy begins by populating informative indicators related to Bitcoin (BTC) on different timeframes, such as daily (1d), 4-hourly (4h), hourly (1h), 15-minute (15m), and 5-minute (5m) timeframes. These indicators provide additional information for analysis.
Next, the strategy merges the informative indicators with the base timeframe (5-minute) indicators of the trading pair.
After merging the indicators, the strategy proceeds to populate the buy trend.
It checks various conditions, including the Relative Strength Index (RSI) being less than 30, the volume being greater than zero, and additional user-defined parameters for buy protection. If these conditions are met, the strategy marks the corresponding data points as potential buy signals. The strategy then populates the sell trend, although the provided code does not contain any specific sell conditions or logic. The "sell" column is set to zero for all data points. Finally, the strategy includes a function that determines whether to execute a buy order based on the current rate and the latest data point in the analyzed dataframe. It checks the slippage (the difference between the desired rate and the actual rate) and cancels the buy order if the slippage exceeds a certain threshold. Additionally, the strategy includes two helper functions for generating conditions based on row data and calculating the Chaikin Money Flow (CMF) indicator. Overall, the "NostalgiaForInfinityX2" strategy utilizes various indicators, buy conditions, and slippage checks to backtest and analyze trading strategies on different timeframes.
Next, the strategy merges the informative indicators with the base timeframe (5-minute) indicators of the trading pair.
After merging the indicators, the strategy proceeds to populate the buy trend.
It checks various conditions, including the Relative Strength Index (RSI) being less than 30, the volume being greater than zero, and additional user-defined parameters for buy protection. If these conditions are met, the strategy marks the corresponding data points as potential buy signals. The strategy then populates the sell trend, although the provided code does not contain any specific sell conditions or logic. The "sell" column is set to zero for all data points. Finally, the strategy includes a function that determines whether to execute a buy order based on the current rate and the latest data point in the analyzed dataframe. It checks the slippage (the difference between the desired rate and the actual rate) and cancels the buy order if the slippage exceeds a certain threshold. Additionally, the strategy includes two helper functions for generating conditions based on row data and calculating the Chaikin Money Flow (CMF) indicator. Overall, the "NostalgiaForInfinityX2" strategy utilizes various indicators, buy conditions, and slippage checks to backtest and analyze trading strategies on different timeframes.
Biased Exit Signals:
20
stoploss: -0.99 timeframe: 5m hash(sha256): fa76b55a012c55d24976573bcc61b4d6739cf7761d89c08f4da3e2e372403f22 indicators: bt_agefilter_ok high open close close_s close res1 high_s low_s btc_info_ pivot USDTBUSDUSDCDAITUSDPAXUSDEURGBP xe5 volume sup3 live_data_ok xe1 date open sup2 5m15m1h4h1d xe4 open_s rsi_14 high low_s open_s close_s low open close sup1 15m1h4h1d not_downtrend xe6 openclosehighlow xe2 stake_currency res2 T3Average high_s open_s close_s res3 runmode low xe3 date open high low close volume
Similar Strategies: (based on used indicators)
Strategy: NostalgiaForInfinityX2_226, Similarity Score: 88.89%
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Strategy: NostalgiaForInfinityX2_2, Similarity Score: 83.33%
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Strategy: NostalgiaForInfinityX2_4, Similarity Score: 83.33%
Strategy: NostalgiaForInfinityX2_5, Similarity Score: 81.48%
Strategy: NostalgiaForInfinityX2_6, Similarity Score: 81.48%
Strategy: NostalgiaForInfinityX2_7, Similarity Score: 81.48%
Strategy: NostalgiaForInfinityX2_8, Similarity Score: 77.78%
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last change: 2023-07-05 06:51:04