The Schism strategy is a trading strategy that aims to generate buy and sell signals based on various indicators. Here is a short description of what the strategy does:
Populating Indicators: The strategy calculates several indicators including Rate of Change (ROC), Relative Strength Index (RSI), Moving Averages Crossover (MAC), Moving Average Streak (MAStreak), Percent Change Channel (PCC), and Relative Momentum Index (RMI) for the given trading data. Buy Trigger Signals: The strategy determines the conditions for generating buy signals.
If there is an active trade, it considers factors such as current profit, peak profit, and the growth of RMI over time.
If there is no active trade, it considers indicators like MP (RSI), streak-roc, and MAC, as well as the volume.
Sell Trigger Signals: The strategy defines the conditions for generating sell signals. If there is an active trade, it considers factors such as current profit, stop loss, RMI trend, and volume. It also considers the presence of other trades and their profitability. If there are no other trades, it checks the volume for generating a sell signal. Custom Methods for Live Trade Data and Real-time Price: The strategy retrieves live trade data and real-time price information. It calculates the current profit, peak profit, open minutes, and open candle count for the active trade. It also checks for other open trades and calculates their average profitability. Additionally, it determines the number of free slots for new trades based on the maximum allowed open trades. Price Protection on Trade Entry and Timeouts: The strategy incorporates price protection on trade entry by comparing the current price with the order price. If the current price is higher than the order price by a certain threshold, it considers it as a protection mechanism and returns a True value. Please note that this description provides a general understanding of the strategy's components and their purpose. Further details and specific calculations may be present within the code implementation.
If there is an active trade, it considers factors such as current profit, peak profit, and the growth of RMI over time.
If there is no active trade, it considers indicators like MP (RSI), streak-roc, and MAC, as well as the volume.
Sell Trigger Signals: The strategy defines the conditions for generating sell signals. If there is an active trade, it considers factors such as current profit, stop loss, RMI trend, and volume. It also considers the presence of other trades and their profitability. If there are no other trades, it checks the volume for generating a sell signal. Custom Methods for Live Trade Data and Real-time Price: The strategy retrieves live trade data and real-time price information. It calculates the current profit, peak profit, open minutes, and open candle count for the active trade. It also checks for other open trades and calculates their average profitability. Additionally, it determines the number of free slots for new trades based on the maximum allowed open trades. Price Protection on Trade Entry and Timeouts: The strategy incorporates price protection on trade entry by comparing the current price with the order price. If the current price is higher than the order price by a certain threshold, it considers it as a protection mechanism and returns a True value. Please note that this description provides a general understanding of the strategy's components and their purpose. Further details and specific calculations may be present within the code implementation.
Traceback (most recent call last): File "/freqtrade/freqtrade/main.py", line 42, in main return_code = args['func'](args) ^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/commands/optimize_commands.py", line 58, in start_backtesting backtesting.start() File "/freqtrade/freqtrade/optimize/backtesting.py", line 1401, in start min_date, max_date = self.backtest_one_strategy(strat, data, timerange) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 1318, in backtest_one_strategy preprocessed = self.strategy.advise_all_indicators(data) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1378, in advise_all_indicators return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1378, inreturn {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1410, in advise_indicators return self.populate_indicators(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/Schism_Badstreak.py", line 82, in populate_indicators dataframe['mac'] = cta.macross(dataframe, 21, 55) ^^^ NameError: name 'cta' is not defined
stoploss: -0.2 timeframe: 5m hash(sha256): 8f107ad38a5228ced480228c6ff47d97e58b6859f8a4cf2af1d62d624afa7789 indicators: active_trade mac close free_slots open_candles rmidntrend other_trades meta mp avg_other_profit volume price_side streak rmiuptrend sbc streakroc rmi pcclowerband high current_profit max_open_trades price open_minutes upperband middleband lowerband roc rmidn biggest_loser peak_profit pccupperband rmiup runmode last low source_type
Similar Strategies: (based on used indicators)
Strategy: Schism_Badstreak2, Similarity Score: 94.59%
Strategy: Stinkfist, Similarity Score: 91.89%
last change: 2024-04-29 01:31:15