The "TaSearch30m" strategy is a trading strategy implemented in Python for backtesting on a backtesting website. Here's a summary of what the strategy does:
It imports the required libraries and modules, including pandas for data manipulation and the necessary classes from the backtesting framework. The strategy class is defined, inheriting from the "IStrategy" interface provided by the backtesting framework.
The strategy has the following parameters: "n" (integer): set to 72 "p" (float): set to 5 The strategy specifies a minimal return on investment (ROI) of 0.03 and a stop loss of -0.1.
The strategy operates on a timeframe of 30 minutes.
In the strategy's initialization method, an instance of the "TaSearch" class is created with the specified parameters. The "populate_indicators" method takes a pandas DataFrame as input and performs the following operations: Renames the columns of the DataFrame. Calls the "find_extremes" method from the "TaSearch" instance to find extreme points in the data. Calls the "buy_past_rsi" and "buy_stride" methods of the strategy to generate additional indicators. The "buy_past_rsi" method iterates over the DataFrame in reverse order and checks if certain conditions are met to identify buy signals based on past RSI values. The "buy_stride" method also iterates over the DataFrame in reverse order and checks if certain conditions are met to identify buy signals based on RSI values and previous extreme points. The "populate_buy_trend" method populates the "buy" column of the DataFrame based on the values of the "buy_stride" and "buy_past_rsi" indicators. The "populate_sell_trend" method populates the "sell" column of the DataFrame based on the value of the RSI indicator. Overall, the strategy aims to identify potential buying opportunities based on past RSI values, RSI values within a specific range, and extreme points in the data. It generates buy and sell signals based on these conditions, which can be used for backtesting and evaluating the strategy's performance.
The strategy has the following parameters: "n" (integer): set to 72 "p" (float): set to 5 The strategy specifies a minimal return on investment (ROI) of 0.03 and a stop loss of -0.1.
The strategy operates on a timeframe of 30 minutes.
In the strategy's initialization method, an instance of the "TaSearch" class is created with the specified parameters. The "populate_indicators" method takes a pandas DataFrame as input and performs the following operations: Renames the columns of the DataFrame. Calls the "find_extremes" method from the "TaSearch" instance to find extreme points in the data. Calls the "buy_past_rsi" and "buy_stride" methods of the strategy to generate additional indicators. The "buy_past_rsi" method iterates over the DataFrame in reverse order and checks if certain conditions are met to identify buy signals based on past RSI values. The "buy_stride" method also iterates over the DataFrame in reverse order and checks if certain conditions are met to identify buy signals based on RSI values and previous extreme points. The "populate_buy_trend" method populates the "buy" column of the DataFrame based on the values of the "buy_stride" and "buy_past_rsi" indicators. The "populate_sell_trend" method populates the "sell" column of the DataFrame based on the value of the RSI indicator. Overall, the strategy aims to identify potential buying opportunities based on past RSI values, RSI values within a specific range, and extreme points in the data. It generates buy and sell signals based on these conditions, which can be used for backtesting and evaluating the strategy's performance.
stoploss: -0.1 timeframe: 30m hash(sha256): 8ee906e0ba218f5f6e8bff695ff4b467b34bb2681587876a1c386bbb7f5ed7c2
Was not able to fetch indicators from Strategyfile.
last change: 2024-04-28 23:04:55