The UpSliceStrategy is a trading strategy designed for backtesting on a trading website. It aims to generate profits by identifying upward price movements and making buy decisions based on certain technical analysis indicators. Here are the key features of the strategy:
Minimal ROI: The strategy has predefined levels of minimal return on investment (ROI) for different time periods.
It aims to achieve a 5% ROI after 240 minutes, 3% after 300 minutes, no ROI after 360 minutes, and 8% immediately.
Stoploss: The strategy sets a stoploss level at -10%.
If the price of the asset drops by 10% from the entry point, the strategy will trigger a sell signal to limit losses. Timeframe: The strategy operates on a 5-minute timeframe, meaning it analyzes price data and makes trading decisions based on 5-minute intervals. Trailing stoploss: The strategy incorporates a trailing stoploss feature. This means that if the price of the asset increases by a certain percentage (0.01 in this case), the stoploss level will adjust to lock in profits. The trailing stoploss positive offset is set at 0.03, indicating the distance the stoploss will trail behind the price. Candle processing: The strategy processes indicators only when a new candle is formed, rather than recalculating them for every tick. Indicators: The strategy uses the TEMA (Triple Exponential Moving Average) indicator, calculated with a time period of 9, to identify trends and potential buying opportunities. Buy signal: The strategy generates a buy signal when the following conditions are met: The current close price is higher than the close price of the previous candle. The close price of the previous candle is higher than the close price of the candle before that. The TEMA indicator is rising. The volume is greater than 0 (to ensure there is trading activity). Sell signal: The strategy does not generate any sell signals based on the provided code. Note: The strategy implementation may have additional features or customizations depending on the configuration file and specific requirements.
It aims to achieve a 5% ROI after 240 minutes, 3% after 300 minutes, no ROI after 360 minutes, and 8% immediately.
Stoploss: The strategy sets a stoploss level at -10%.
If the price of the asset drops by 10% from the entry point, the strategy will trigger a sell signal to limit losses. Timeframe: The strategy operates on a 5-minute timeframe, meaning it analyzes price data and makes trading decisions based on 5-minute intervals. Trailing stoploss: The strategy incorporates a trailing stoploss feature. This means that if the price of the asset increases by a certain percentage (0.01 in this case), the stoploss level will adjust to lock in profits. The trailing stoploss positive offset is set at 0.03, indicating the distance the stoploss will trail behind the price. Candle processing: The strategy processes indicators only when a new candle is formed, rather than recalculating them for every tick. Indicators: The strategy uses the TEMA (Triple Exponential Moving Average) indicator, calculated with a time period of 9, to identify trends and potential buying opportunities. Buy signal: The strategy generates a buy signal when the following conditions are met: The current close price is higher than the close price of the previous candle. The close price of the previous candle is higher than the close price of the candle before that. The TEMA indicator is rising. The volume is greater than 0 (to ensure there is trading activity). Sell signal: The strategy does not generate any sell signals based on the provided code. Note: The strategy implementation may have additional features or customizations depending on the configuration file and specific requirements.
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/UpSliceStrategy_292.py", line 87, in populate_buy_trend (dataframe['close'].shift > dataframe['close'].shift(2)) & ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/ops/common.py", line 76, in new_method return method(self, other) ^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/arraylike.py", line 48, in __lt__ return self._cmp_method(other, operator.lt) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/series.py", line 5803, in _cmp_method res_values = ops.comparison_op(lvalues, rvalues, op) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/ops/array_ops.py", line 349, in comparison_op res_values = _na_arithmetic_op(lvalues, rvalues, op, is_cmp=True) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/ops/array_ops.py", line 220, in _na_arithmetic_op result = func(left, right) ^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/computation/expressions.py", line 242, in evaluate return _evaluate(op, op_str, a, b) # type: ignore[misc] ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/computation/expressions.py", line 131, in _evaluate_numexpr result = _evaluate_standard(op, op_str, a, b) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/computation/expressions.py", line 73, in _evaluate_standard return op(a, b) ^^^^^^^^ TypeError: '<' not supported between instances of 'float' and 'method'
stoploss: -0.1 timeframe: 5m hash(sha256): cf3e9ea5f4f0c3ebb2dd061ec1abb57e1039be82d7c34e15fc42b9ad54c15a6c indicators: volume close tema
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last change: 2024-07-28 21:32:19