Overall Statistics |
Total Trades 340 Average Win 0.24% Average Loss -0.21% Compounding Annual Return 7.466% Drawdown 10.100% Expectancy 0.405 Net Profit 24.112% Sharpe Ratio 0.647 Probabilistic Sharpe Ratio 22.435% Loss Rate 35% Win Rate 65% Profit-Loss Ratio 1.15 Alpha 0.07 Beta -0.228 Annual Standard Deviation 0.085 Annual Variance 0.007 Information Ratio -0.089 Tracking Error 0.155 Treynor Ratio -0.241 Total Fees $362.52 Estimated Strategy Capacity $890000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X |
# This code is provided for informational purposes only. # Do NOT trade using it or you WILL loose money. from SimpleLinearRegressionChannel import SimpleLinearRegressionChannel class SimpleLinearRegressionChannelAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2016, 1, 1) self.SetEndDate(2018, 12, 31) self.SetCash(100000) self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol self.slrc = SimpleLinearRegressionChannel(self, 26, 26, 3.0) self.SetWarmUp(self.slrc.WarmUpPeriod, Resolution.Daily) def OnData(self, data): if data.ContainsKey(self.spy) and data[self.spy] is not None: self.slrc.Update(data[self.spy]) else: return if not self.slrc.IsReady: return # Since our indicator is ready, we can use it to compare the current bar with the simple linear regression projection. (low, mid, high) = self.slrc.GetProjection() # self.Debug(f"({low}, {mid}, {high})") bar = data[self.spy] minBarBody = min(bar.Open, bar.Close) maxBarBody = max(bar.Open, bar.Close) posSlope = self.slrc.GetSlope() > 0.0 if posSlope: if minBarBody > mid: self.SetHoldings(self.spy, 1) elif minBarBody > low: self.SetHoldings(self.spy, 0.7) elif minBarBody < low and maxBarBody > low: self.SetHoldings(self.spy, 0.3) elif maxBarBody < low: self.Liquidate(self.spy) else: if maxBarBody < mid: self.SetHoldings(self.spy, -1) elif maxBarBody < high: self.SetHoldings(self.spy, -0.7) elif maxBarBody > high and minBarBody < high: self.SetHoldings(self.spy, -0.3) elif minBarBody > high: self.Liquidate(self.spy)
# This code is provided for informational purposes only. # Do NOT trade using it or you WILL loose money. from collections import deque from statistics import stdev class SimpleLinearRegressionChannel(PythonIndicator): def __init__(self, algorithm: QCAlgorithm, base_period: int, projection_period: int, channel_width: float): super().__init__() assert base_period > 0, f"{self.__init__.__qualname__}: base_period must be greater than 0." assert projection_period > 0, f"{self.__init__.__qualname__}: projection_period must be greater than 0." assert channel_width >= 0.0, f"{self.__init__.__qualname__}: channel_width must be greater than or equal to 0.0." if base_period < 10: algorithm.Log(f"Warning - {self.__init__.__qualname__}: base_period is less than 10. This is very few data points to compute a simple linear regression.") self._algorithm = algorithm self._base_period = base_period self._x = list(range(1, base_period + 1)) self._x_sum = sum(self._x) self._x_mean = self._x_sum / base_period self._diffs_x_mean = [(x_i - self._x_mean) for x_i in self._x] self._B1_den = sum(pow(x_i, 2) for x_i in self._diffs_x_mean) self._projection_period = projection_period self._channel_width = channel_width self._stdev = None self.Value = None self._base_window = deque(maxlen=base_period) self._B0 = None self._B1 = None self._projection_window = deque(maxlen=projection_period) self._R_den_x = (base_period * sum(pow(x_i, 2) for x_i in self._x)) - pow(self._x_sum, 2) self.WarmUpPeriod = base_period @property def IsReady(self): return (len(self._base_window) == self._base_window.maxlen) and (len(self._projection_window) >= 1) def Update(self, _input): if len(self._base_window) != self._base_window.maxlen: self._base_window.append(_input.Close) else: projection_size = len(self._projection_window) if projection_size == 0: self._simple_linreg() if projection_size != self._projection_window.maxlen: self._projection_window.append(_input.Close) else: self._reset(_input) def _simple_linreg(self): y_mean = sum(self._base_window) / self._base_period B1_num = sum((x_j * y_j) for x_j, y_j in zip(self._diffs_x_mean, [(y_i - y_mean) for y_i in self._base_window])) self._B1 = B1_num / self._B1_den self._B0 = y_mean - (self._B1 * self._x_mean) self._stdev = stdev(self._base_window) def _reset(self, _input): self._base_window.clear() if self._base_period > self._projection_period: self._base_window.extend(self._projection_window) self._base_window.append(_input.Close) self._projection_window.clear() else: while len(self._base_window) != self._base_window.maxlen: self._base_window.append(self._projection_window.popleft()) self._simple_linreg() self._projection_window.append(_input.Close) def GetSlope(self): if self.IsReady: return self._B1 else: return None def GetProjection(self): if self.IsReady: x = self._base_period + len(self._projection_window) y = self._B0 + (self._B1 * x) channel = self._channel_width * self._stdev return (y - channel, y, y + channel) else: return (None, None, None) def GetCorrelationCoefficient(self): if self.IsReady: num = (self._base_period * sum((x_i * y_i) for x_i, y_i in zip(self._x, self._base_window))) - (self._x_sum * sum(self._base_window)) den = math.sqrt(self._R_den_x * ((self._base_period * sum(pow(y_i, 2) for y_i in self._base_window)) - pow(sum(self._base_window), 2))) return num / den else: return None