Strategy: BbStage
Downloaded: 20220112
Stoploss: -0.2
The BbStage strategy is a backtesting strategy that uses a combination of technical indicators to determine buy and sell signals for trading. Here is a breakdown of its key components: Timeframe: The strategy operates on 1-hour candlestick data. Indicators: Relative Strength Index (RSI): Calculates the RSI indicator with a time period of 14.

Exponential Moving Averages (EMA): Calculates three EMAs with time periods of 9, 20, and 200.

Bollinger Bands: Calculates the middle band and upper band of the Bollinger Bands indicator using a window of 20 and a standard deviation of 2.

Buy Signal: The close price should be above the middle band of the Bollinger Bands. The close price should be below the upper band of the Bollinger Bands. The close price should be above the 9-day EMA. The close price should be above the 200-day EMA. The 20-day EMA should be above the 200-day EMA. Sell Signal: The RSI should be above 75. The close price should be below 97% of the middle band of the Bollinger Bands. The open price should be higher than the close price (red bar). Risk Management: Minimal ROI (Return on Investment): The strategy aims for a minimum ROI of 20%. Stop Loss: A stop loss of 0.2 (20%) is used to limit potential losses. Trailing Stop: A trailing stop is employed to protect profits. It trails positively by 5% and activates after a 15% increase in price. Order Types: Market orders are used for buying and selling, while a limit order is used for the stop loss. Locking Pairs: If running in live or dry run mode, the strategy checks the past performance of closed trades within the last 4 hours for the trading pair. If the total profit from those trades exceeds 10% of the stake amount, the pair is locked for 4 hours to prevent further trading. Note: This description provides a general understanding of the strategy, but the implementation details may require further analysis and testing.

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 1335, in backtest_one_strategy results = self.backtest( ^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 1213, in backtest data: Dict = self._get_ohlcv_as_lists(processed) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 381, in _get_ohlcv_as_lists df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair}) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1391, in ft_advise_signals dataframe = self.advise_entry(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1425, in advise_entry df = self.populate_entry_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 225, in populate_entry_trend return self.populate_buy_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/BbStage.py", line 67, in populate_buy_trend sumprofit = sum(trade.close_profit for trade in trades) ^^^^^^ UnboundLocalError: cannot access local variable 'trades' where it is not associated with a value
stoploss: -0.2
timeframe: 1h
hash(sha256): 65da7f53b4af3d6909ae14ccbc49e4d230d5798e273296f98e0f88ce9d05ef4f
upper mid stake_amount ema20 bb_middleband
rsi close bb_upperband runmode open
ema9 ema200

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last change: 2024-04-29 02:57:30