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The given code snippet appears to define a trading strategy called "HeikinAshi." Here's a short description of what the strategy does:
The strategy is implemented as a class that inherits from the "IStrategy" class. It consists of three main functions: "populate_indicators," "populate_buy_trend," and "populate_sell_trend." These functions are used to generate trading signals based on various conditions. In the "populate_indicators" function, the strategy calculates certain indicators based on the input dataframe and metadata.
It merges informative data from a 1-hour timeframe and fills any missing values.
The "populate_buy_trend" function sets the "buy" signal to 1 when specific conditions are met.
These conditions include the presence of data from the 1-hour timeframe and a positive volume. Similarly, the "populate_sell_trend" function sets the "sell" signal to 1 when conditions are met. These conditions include the presence of data from the 1-hour timeframe and a positive volume. After defining these functions, the strategy performs calculations on the input dataframe to generate additional indicators such as moving averages (EMAs) and other derived values. These indicators are used to determine the trading signals. The strategy calculates two main variables, "mavi" and "kirmizi," based on the calculated indicators. It then checks for specific conditions to determine whether a long or short position should be taken. The "state" variable keeps track of the current state of the strategy, and the "longCond" and "shortCond" variables store the final trading signals based on the determined conditions. Finally, the strategy returns a Series named "has," which represents the final trading signal with values of 1 for long positions, -1 for short positions, and 0 for no position. Note: The code snippet provided lacks some necessary imports and variable definitions, so it may not run as is.
It merges informative data from a 1-hour timeframe and fills any missing values.
The "populate_buy_trend" function sets the "buy" signal to 1 when specific conditions are met.
These conditions include the presence of data from the 1-hour timeframe and a positive volume. Similarly, the "populate_sell_trend" function sets the "sell" signal to 1 when conditions are met. These conditions include the presence of data from the 1-hour timeframe and a positive volume. After defining these functions, the strategy performs calculations on the input dataframe to generate additional indicators such as moving averages (EMAs) and other derived values. These indicators are used to determine the trading signals. The strategy calculates two main variables, "mavi" and "kirmizi," based on the calculated indicators. It then checks for specific conditions to determine whether a long or short position should be taken. The "state" variable keeps track of the current state of the strategy, and the "longCond" and "shortCond" variables store the final trading signals based on the determined conditions. Finally, the strategy returns a Series named "has," which represents the final trading signal with values of 1 for long positions, -1 for short positions, and 0 for no position. Note: The code snippet provided lacks some necessary imports and variable definitions, so it may not run as is.
Biased Indicators
has_1hBiased Entry Signals:
20Biased Exit Signals:
20
stoploss: -0.1 timeframe: 5m hash(sha256): 80c5902da83538ba8d30b0038bf89eff009907f0fa4cfd60b7ee176626c509da indicators: has high volume ohlc4 hlc3 has_1h low open high low close high low close
No similar strategies found. (based on used indicators)
last change: 2023-06-30 12:20:30