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Strategy: FreqGym_normalized_2
Downloaded: 20230211
Stoploss: -0.99
The FreqGymNormalized strategy is a trading strategy that performs backtesting using various technical analysis (TA) indicators. Here's a short description of what the strategy does: The populate_indicators method takes raw data from the exchange and adds several TA indicators to the DataFrame. These indicators include plus_di, minus_di, uo, htsine, htleadsine, bop, slowk, slowd, fastk, bb2_lower_gt_close, bb3_lower_gt_close, adx, aroonup, aroondown, aroonosc, cmo, dx, mfi, minus_di, plus_di, willr, rsi, fisher_rsi, stochrsi_k, stochrsi_d, and linangle.

The populate_buy_trend method generates the buy signal for the given DataFrame based on the TA indicators and an RL (reinforcement learning) model's prediction.

The populate_sell_trend method generates the sell signal for the given DataFrame based on the TA indicators and an RL model's prediction.

The strategy uses the RL model to predict the actions (buy/sell) based on the indicators. The buy and sell signals are added to the DataFrame as buy and sell columns, respectively. The strategy also includes some logging statements to track the progress and performance of indicator calculation and signal generation. This strategy aims to generate buy and sell signals based on the provided TA indicators and the RL model's predictions, allowing for backtesting and evaluation of the strategy's performance.

stoploss: -0.99
timeframe: 5m
hash(sha256): 4cc272282864023e2c3795f9e9c4459a09196dbe8e48953224d75711d1a560a9
indicators:
sell htsine plus_di_period willr_period mfi_period
close aroonup_period aroondown_period uo dx_period
rsi_period aroondown fastk htleadsine slowd
leadsine stochrsi_d_period bb3_lower_gt_close minus_di_period plus_di
fastd aroonosc_period cmo_period slowk bb2_lower_gt_close
adx_period fisher_rsi_period buy stochrsi_k_period aroonup
minus_di lower linangle_period bop sine

Similar Strategies: (based on used indicators)

Strategy: FreqGym_normalized_412, Similarity Score: 97.22%
Strategy: FreqGym_normalized_0, Similarity Score: 94.44%
Strategy: FreqGym_normalized_3, Similarity Score: 94.44%
Strategy: FreqGym_normalized, Similarity Score: 91.67%

last change: 2024-01-24 21:50:56