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The "NormalizerStrategy" is a trading strategy implemented in Python using the Freqtrade library. Here's a short description of what the strategy does:
The strategy is designed for backtesting trading strategies on historical price data. It uses the 1-hour timeframe for analysis.
The strategy calculates and populates various indicators based on the closing price of the asset using the Fischer normalization method.
The Fischer normalization is applied with different lookback periods (13, 21, 34, 55, 89, 144, 233, 377, and 610).
The strategy sums up the normalized values from the indicators to create a "pct_sum" column in the dataframe. For the buy signal, the strategy looks for conditions where the "pct_sum" value is less than 0.2 and the volume is greater than 0. For the sell signal, the strategy looks for conditions where the "pct_sum" value is greater than 8 and the volume is greater than 0. The strategy uses a minimal ROI of 0.18 and a stop-loss value of -0.99, effectively disabling it. It employs a trailing stop-loss mechanism with a positive offset of 0.01 and a positive offset reach of 0.015. The strategy has a custom stop-loss function that manages losing trades by closing them with a 0.01 stop-loss if the trade has been open for more than 300 minutes. It requires at least 610 candles (historical data points) before producing valid signals. The strategy uses limit orders for buying and selling, and market orders for stop-loss. It ignores the ROI requirement if there is a buy signal. Overall, the strategy aims to identify buying opportunities when the sum of normalized indicators is low and selling opportunities when the sum is high. It incorporates a trailing stop-loss and a custom stop-loss function to manage trades.
The strategy calculates and populates various indicators based on the closing price of the asset using the Fischer normalization method.
The Fischer normalization is applied with different lookback periods (13, 21, 34, 55, 89, 144, 233, 377, and 610).
The strategy sums up the normalized values from the indicators to create a "pct_sum" column in the dataframe. For the buy signal, the strategy looks for conditions where the "pct_sum" value is less than 0.2 and the volume is greater than 0. For the sell signal, the strategy looks for conditions where the "pct_sum" value is greater than 8 and the volume is greater than 0. The strategy uses a minimal ROI of 0.18 and a stop-loss value of -0.99, effectively disabling it. It employs a trailing stop-loss mechanism with a positive offset of 0.01 and a positive offset reach of 0.015. The strategy has a custom stop-loss function that manages losing trades by closing them with a 0.01 stop-loss if the trade has been open for more than 300 minutes. It requires at least 610 candles (historical data points) before producing valid signals. The strategy uses limit orders for buying and selling, and market orders for stop-loss. It ignores the ROI requirement if there is a buy signal. Overall, the strategy aims to identify buying opportunities when the sum of normalized indicators is low and selling opportunities when the sum is high. It incorporates a trailing stop-loss and a custom stop-loss function to manage trades.
Biased Indicators
pct_sum
stoploss: -0.99 timeframe: 1h hash(sha256): e5a31fd028be6e981f026cb56c8ea08e6fe2e52d2b933c6c54b419514bb6fdd2 indicators: pct_sum volume f"norm_look
No similar strategies found. (based on used indicators)
last change: 2023-06-28 07:11:37