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The LongShortRangeTradingMachetesV1 strategy is designed to backtest trading strategies using various indicators. Here's a breakdown of what the strategy does:
The populate_indicators function populates the indicators on the provided dataframe, using the on_populate_indicators function if available. The populate_buy_trend function populates the buy signals on the dataframe based on the entry_signal indicator.
It sets the 'buy' column to 1 when the entry_signal is 1.
The populate_sell_trend function populates the sell signals on the dataframe based on the exit_signal indicator.
It sets the 'sell' column to 1 when the exit_signal is 1. The strategy calculates additional indicators such as RMI (Relative Momentum Index), SSL Channels with ATR (Average True Range), and SROC (Smoothed Rate of Change) using various mathematical calculations and technical analysis libraries. It assigns values to specific columns in the dataframe based on the calculated indicators and their corresponding conditions. The strategy incorporates CCI (Commodity Channel Index) and its overbought and oversold values to determine entry and exit signals. The strategy tracks the market condition, confirmation state, and trade flow state using specific flags and conditions. Finally, the strategy iterates over the provided dataframe, updating the trade flow state based on market conditions and confirmation signals. It sets the entry and exit signals accordingly. Overall, the strategy combines multiple indicators and conditions to generate buy and sell signals based on the provided dataframe's data. It follows a specific trade flow state and tracks market conditions to determine the appropriate actions.
It sets the 'buy' column to 1 when the entry_signal is 1.
The populate_sell_trend function populates the sell signals on the dataframe based on the exit_signal indicator.
It sets the 'sell' column to 1 when the exit_signal is 1. The strategy calculates additional indicators such as RMI (Relative Momentum Index), SSL Channels with ATR (Average True Range), and SROC (Smoothed Rate of Change) using various mathematical calculations and technical analysis libraries. It assigns values to specific columns in the dataframe based on the calculated indicators and their corresponding conditions. The strategy incorporates CCI (Commodity Channel Index) and its overbought and oversold values to determine entry and exit signals. The strategy tracks the market condition, confirmation state, and trade flow state using specific flags and conditions. Finally, the strategy iterates over the provided dataframe, updating the trade flow state based on market conditions and confirmation signals. It sets the entry and exit signals accordingly. Overall, the strategy combines multiple indicators and conditions to generate buy and sell signals based on the provided dataframe's data. It follows a specific trade flow state and tracks market conditions to determine the appropriate actions.
startup_candle_count : 500 Recursive Analysis found no issues while using 500 startup_candle_count
stoploss: -0.1 timeframe: 1m hash(sha256): 33f0c77297f307e7dc7d88b6ffcdd07939c5812852fad7ec9fb3909fc6f0ccc1 indicators: has_entry_signal cci_oversold_value active_trade dataframe selfdataframe close emaDec has_exit_signal date candleuptrend entry_signal maxdown date ssldir candleup price_side smaHigh rmi ssl candle any ATR rmiuptrend sslDown date maxup date roc base_indicator_key_timeframe_str hlv candleuptrend trade_flow rmi smaLow has_entered_market_condition high sslUp ssldir has_confirmation date sroc cci exit_signal oversold_price overbought_price cci_overbought_value base__name atr sroc roc date rmiuptrend
No similar strategies found. (based on used indicators)
last change: 2023-10-22 21:47:18