The FrostAuraBifrostStrategy is an AI-powered trading strategy that utilizes the FrostAura Bifrost API. It backtests various trading strategies to optimize profitability based on historical data. Key features of the strategy include:
Minimal ROI: The strategy aims to achieve a minimal return on investment (ROI) based on predefined target values at different time intervals.
Stoploss: The strategy incorporates an optimal stoploss value to limit potential losses.
Trailing stoploss: The strategy does not utilize a trailing stoploss feature.
Timeframe: The strategy operates on a 1-hour timeframe for analyzing price data. Candle count: The strategy requires a minimum number of candles before producing valid trading signals. Buy and sell signals: The strategy generates buy and sell signals based on predicted price deltas obtained from the Bifrost API. Order type mapping: The strategy uses market orders for buying, selling, and stoploss orders. Order time in force: The strategy specifies "good 'til canceled" (GTC) as the order time in force for both buy and sell orders. Additionally, the strategy includes a plot configuration that visualizes technical indicators such as TEMA, SAR, MACD, and RSI. The strategy utilizes the populate_indicators method to retrieve bulk predictions from the Bifrost API and populate the dataframe with the delta percentages. The populate_buy_trend and populate_sell_trend methods use the delta percentages to determine when to generate buy and sell signals based on user-defined parameters. Overall, the FrostAuraBifrostStrategy aims to optimize profitability by analyzing price data and generating signals based on AI-powered predictions from the FrostAura Bifrost API.
Stoploss: The strategy incorporates an optimal stoploss value to limit potential losses.
Trailing stoploss: The strategy does not utilize a trailing stoploss feature.
Timeframe: The strategy operates on a 1-hour timeframe for analyzing price data. Candle count: The strategy requires a minimum number of candles before producing valid trading signals. Buy and sell signals: The strategy generates buy and sell signals based on predicted price deltas obtained from the Bifrost API. Order type mapping: The strategy uses market orders for buying, selling, and stoploss orders. Order time in force: The strategy specifies "good 'til canceled" (GTC) as the order time in force for both buy and sell orders. Additionally, the strategy includes a plot configuration that visualizes technical indicators such as TEMA, SAR, MACD, and RSI. The strategy utilizes the populate_indicators method to retrieve bulk predictions from the Bifrost API and populate the dataframe with the delta percentages. The populate_buy_trend and populate_sell_trend methods use the delta percentages to determine when to generate buy and sell signals based on user-defined parameters. Overall, the FrostAuraBifrostStrategy aims to optimize profitability by analyzing price data and generating signals based on AI-powered predictions from the FrostAura Bifrost API.
Traceback (most recent call last): File "/usr/local/lib/python3.11/urllib/request.py", line 1348, in do_open h.request(req.get_method(), req.selector, req.data, headers, File "/usr/local/lib/python3.11/http/client.py", line 1298, in request self._send_request(method, url, body, headers, encode_chunked) File "/usr/local/lib/python3.11/http/client.py", line 1344, in _send_request self.endheaders(body, encode_chunked=encode_chunked) File "/usr/local/lib/python3.11/http/client.py", line 1293, in endheaders self._send_output(message_body, encode_chunked=encode_chunked) File "/usr/local/lib/python3.11/http/client.py", line 1052, in _send_output self.send(msg) File "/usr/local/lib/python3.11/http/client.py", line 990, in send self.connect() File "/usr/local/lib/python3.11/http/client.py", line 956, in connect self.sock = self._create_connection( ^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/socket.py", line 827, in create_connection for res in getaddrinfo(host, port, 0, SOCK_STREAM): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/socket.py", line 962, in getaddrinfo for res in _socket.getaddrinfo(host, port, family, type, proto, flags): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ socket.gaierror: [Errno -2] Name or service not known During handling of the above exception, another exception occurred: 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/fa_bifrost_strategy.py", line 86, in populate_indicators predictions = self.__get_bifrost_bulk_prediction__(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/fa_bifrost_strategy.py", line 77, in __get_bifrost_bulk_prediction__ response_string = urllib.request.urlopen(bifrost_request_url).read() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/urllib/request.py", line 216, in urlopen return opener.open(url, data, timeout) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/urllib/request.py", line 519, in open response = self._open(req, data) ^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/urllib/request.py", line 536, in _open result = self._call_chain(self.handle_open, protocol, protocol + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/urllib/request.py", line 496, in _call_chain result = func(*args) ^^^^^^^^^^^ File "/usr/local/lib/python3.11/urllib/request.py", line 1377, in http_open return self.do_open(http.client.HTTPConnection, req) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/urllib/request.py", line 1351, in do_open raise URLError(err) urllib.error.URLError:
stoploss: -0.329 timeframe: 1h hash(sha256): 42f25253eca977127a42e652831970a55a7ac302de62b0805e20609d7759c8f4 indicators: data delta_percentage
No similar strategies found. (based on used indicators)
last change: 2024-04-27 23:51:22