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110 lines
3.4 KiB
110 lines
3.4 KiB
4 years ago
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'''
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'''
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from PyQt5.Qt import pyqtSignal
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from PyQt5 import QtCore
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import random
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class CudaSolver():
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'''
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'''
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progress = pyqtSignal(int, int, name='progress')
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done = pyqtSignal(list, name='done')
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def __init__(self, calc, series):
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'''
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Constructor
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'''
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super().__init__(None)
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self.calc = calc
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self.series = series
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self.generators = []
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self.counter = 0
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self.total_counter = 0
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def add_generator(self, generator):
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self.generators.append(generator)
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@QtCore.pyqtSlot(dict)
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def solve(self, params):
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max_signals = 5
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max_hold_bars = params.get('max_hold_bars', 1)
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#self.executor = Executor(self.series, max_hold_bars)
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max_strategies = params.get('num_strategies', 1000)
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results = []
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min_trades = params.get('min_trades', 0)
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min_win_rate = params.get('min_win_rate', 0)
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min_sharpe = params.get('min_sharpe', 0)
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stop = params.get('stop_loss', None)
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tp = params.get('take_profit', None)
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is_long = params.get('direction', 'long') == 'long'
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while len(results) < max_strategies:
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sig_num = random.randint(1, max_signals)
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strategy = []
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for i in range(0, sig_num):
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strategy.append(random.choice(self.generators).generate())
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trades = self.executor.execute(strategy, is_long, stop, tp)
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if len(trades) >= min_trades:
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result = self.evaluate_trades(trades)
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if result['win_percentage'] > min_win_rate and result['sharpe'] > min_sharpe:
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result['strategy'] = strategy
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result['display_name'] = ' && '.join([signal.get_text() for signal in strategy])
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result['trades'] = trades
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results.append(result)
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self.progress.emit(len(results), max_strategies)
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self.done.emit([result])
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self.counter += 1
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self.total_counter += 1
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def evaluate_trades(self, trades):
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result = {}
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profits = [x.pnl() for x in trades]
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total_won = len(list(filter(lambda x: x.pnl() > 0, trades)))
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if len(trades) > 0:
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result['win_percentage'] = total_won / len(trades) * 100
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else:
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result['win_percentage'] = 0
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result['trades_number'] = len(trades)
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result['total_pnl'] = sum(profits)
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if len(trades) > 0:
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result['avg_percentage'] = sum([trade.pnl_percentage() for trade in trades]) / len(trades)
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else:
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result['avg_percentage'] = 0
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gross_profit = sum([max(0, x.pnl()) for x in trades])
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gross_loss = sum([min(0, x.pnl()) for x in trades])
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if gross_loss != 0:
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result['profit_factor'] = gross_profit / (-gross_loss)
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else:
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result['profit_factor'] = inf
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if len(profits) > 0:
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mean = numpy.mean(profits)
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stddev = numpy.std(profits)
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if stddev != 0:
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result['sharpe'] = mean / stddev
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else:
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result['sharpe'] = 0
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else:
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result['sharpe'] = 0
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return result
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