The DNSAlarm strategy is a backtesting trading strategy that aims to identify buying and selling opportunities based on certain conditions. Here is a breakdown of the strategy's main components:
populate_indicators() method: This method calculates and populates various indicators based on the provided data frame. It creates and updates indicators such as bull_engulf_red_line, bear_engulf_red_line, bull_engulf_green_line, and bear_engulf_green_line.
bull_engulf_green_line_list() method: This method generates a list of green lines derived from bull engulf patterns.
bear_engulf_green_line_list() method: This method generates a list of green lines derived from bear engulf patterns.
alarm_lines() method: This method triggers an alarm when certain conditions are met. It checks if the current price is within the alert range and emits an alarm message accordingly. calculate_bull_engulf_red_line() method: This method calculates the red line for the bull engulf pattern based on the previous range and the provided data frame. calculate_bear_engulf_red_line() method: This method calculates the red line for the bear engulf pattern based on the previous range and the provided data frame. should_buy() method: This method determines whether the strategy should initiate a buy trade based on the current closing price, closest bull green line, and closest bull red line. should_stop_loss() method: This method checks if the ongoing closing price reaches the stop-loss price, indicating a potential exit condition for an open trade. should_profit() method: This method checks if the ongoing closing price reaches a target profit rate, indicating a potential exit condition for an open trade. open_trade() method: This method opens a new trade by specifying the buy price and stop-loss price. close_trade() method: This method closes an open trade. populate_buy_trend() method: This method populates the buy column in the data frame with a value of 1 when the buy criteria are met. populate_sell_trend() method: This method populates the sell column in the data frame when the sell criteria are met. Overall, the DNSAlarm strategy analyzes indicators, checks buy and sell criteria, manages trades, and generates alerts when specific conditions are satisfied. It is designed for backtesting trading strategies on historical data.
bull_engulf_green_line_list() method: This method generates a list of green lines derived from bull engulf patterns.
bear_engulf_green_line_list() method: This method generates a list of green lines derived from bear engulf patterns.
alarm_lines() method: This method triggers an alarm when certain conditions are met. It checks if the current price is within the alert range and emits an alarm message accordingly. calculate_bull_engulf_red_line() method: This method calculates the red line for the bull engulf pattern based on the previous range and the provided data frame. calculate_bear_engulf_red_line() method: This method calculates the red line for the bear engulf pattern based on the previous range and the provided data frame. should_buy() method: This method determines whether the strategy should initiate a buy trade based on the current closing price, closest bull green line, and closest bull red line. should_stop_loss() method: This method checks if the ongoing closing price reaches the stop-loss price, indicating a potential exit condition for an open trade. should_profit() method: This method checks if the ongoing closing price reaches a target profit rate, indicating a potential exit condition for an open trade. open_trade() method: This method opens a new trade by specifying the buy price and stop-loss price. close_trade() method: This method closes an open trade. populate_buy_trend() method: This method populates the buy column in the data frame with a value of 1 when the buy criteria are met. populate_sell_trend() method: This method populates the sell column in the data frame when the sell criteria are met. Overall, the DNSAlarm strategy analyzes indicators, checks buy and sell criteria, manages trades, and generates alerts when specific conditions are satisfied. It is designed for backtesting trading strategies on historical data.
stoploss: -0.99 timeframe: 1w hash(sha256): 9a5e0288c75394b9817ae363afdfdeeac26b081d1ce13aa2bf68fce31e2e1b62 indicators: max_high_to_end profit_rate altcoins_restart_alert_percentage buy_criteria close sell_criteria min_low_to_end altcoins_alert_percentage btc_eth_alert_percentage btc_eth_restart_alert_percentage
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last change: 2024-04-29 02:04:16