edgeyopt/solver/solver.py

'''
'''
from data import series
from execution.executor import Executor

import random
from math import inf
import numpy
from PyQt5.Qt import pyqtSignal, QObject
from PyQt5 import QtCore
import talib

class Solver(QObject):
    '''
    
    '''
    
    progress = pyqtSignal(int, int, name='progress')
    done = pyqtSignal(list, name='done')

    def __init__(self, series):
        '''
        Constructor
        '''
        super().__init__(None)
        
        self.series = series
        self.executor = Executor(series)
        self.generators = []
        
    def add_generator(self, generator):
        self.generators.append(generator)
    
    @QtCore.pyqtSlot(dict)
    def solve(self, params):        
        max_signals = 5
        max_strategies = params.get('num_strategies', 1000)
        results = []
        
        min_trades = params.get('min_trades', 0)
        min_win_rate = params.get('min_win_rate', 0)
        min_sharpe = params.get('min_sharpe', 0)
        
        is_long = params.get('direction', 'long') == 'long'
        print(is_long)
        while len(results) < max_strategies:
            sig_num = random.randint(1, max_signals)
            strategy = []
            for i in range(0, sig_num):
                strategy.append(random.choice(self.generators).generate())
                
            trades = self.executor.execute(strategy, is_long)
            if len(trades) >= min_trades:
                result = self.evaluate_trades(trades)
                if result['win_percentage'] > min_win_rate and result['sharpe'] > min_sharpe:
                    result['strategy'] = strategy
                    result['display_name'] = ' && '.join([signal.get_text() for signal in strategy])
                    result['trades'] = trades
                    results.append(result)
                    self.progress.emit(len(results), max_strategies)
            
        self.done.emit(results)
            
    def evaluate_trades(self, trades):
        result = {}
        
        profits = [x.pnl() for x in trades]
        
        total_won = len(list(filter(lambda x: x.pnl() > 0, trades)))

        if len(trades) > 0:
            result['win_percentage'] = total_won / len(trades) * 100
        else:
            result['win_percentage'] = 0
        
        result['trades_number'] = len(trades)
        result['total_pnl'] = sum(profits)
        
        if len(trades) > 0:
            result['avg_percentage'] = sum([trade.pnl_percentage() for trade in trades]) / len(trades)
        else:
            result['avg_percentage'] = 0
        
        gross_profit = sum([max(0, x.pnl()) for x in trades])
        gross_loss = sum([min(0, x.pnl()) for x in trades])
        
        if gross_loss != 0:
            result['profit_factor'] = gross_profit / (-gross_loss)
        else:
            result['profit_factor'] = inf
            
        if len(profits) > 0:
            mean = numpy.mean(profits)
            stddev = numpy.std(profits)
            if stddev != 0:
                result['sharpe'] = mean / stddev
            else:
                result['sharpe'] = 0
        else:
            result['sharpe'] = 0
            
        return result