Hey,

I built a simple trading algorithm, that uses Linear Regression to predict future returns of an equity.

I am using the optimiziation API to do the trading and added the DollarNeutral Constraint, however, no matter how low I set the tolerance, the Net Dollar Exposure peaks at over 10% during the Backtest (see attached). What am I missing?

Clone Algorithm

2

Loading...

There was an error loading this backtest.

Backtest from
to
with
initial capital

Cumulative performance:

Algorithm
Benchmark

Custom data:

Total Returns

--

Alpha

--

Beta

--

Sharpe

--

Sortino

--

Max Drawdown

--

Benchmark Returns

--

Volatility

--

Returns | 1 Month | 3 Month | 6 Month | 12 Month |

Alpha | 1 Month | 3 Month | 6 Month | 12 Month |

Beta | 1 Month | 3 Month | 6 Month | 12 Month |

Sharpe | 1 Month | 3 Month | 6 Month | 12 Month |

Sortino | 1 Month | 3 Month | 6 Month | 12 Month |

Volatility | 1 Month | 3 Month | 6 Month | 12 Month |

Max Drawdown | 1 Month | 3 Month | 6 Month | 12 Month |

from sklearn.linear_model import LinearRegression, ridge_regression import pandas as pd import numpy as np import time from sklearn import svm from sklearn.ensemble import GradientBoostingRegressor from quantopian.pipeline.filters import QTradableStocksUS from quantopian.pipeline.experimental import risk_loading_pipeline from quantopian.pipeline import Pipeline from quantopian.pipeline.factors import SimpleMovingAverage, Returns, CustomFactor from quantopian.pipeline.data import USEquityPricing import quantopian.algorithm as algo import quantopian.optimize as opt import sklearn def initialize(context): context.optimize_portfolio=True context.trained = False context.max_leverage = 1.0 context.max_pos_size = 0.015 context.max_turnover = 0.65 context.train_days_count = 2000 log.info(context.train_days_count) context.input_fields = ['close','open','high','low','volume','sma_10','sma_30'] context.model = LinearRegression() #ridge_regression()#svm.SVR()#GradientBoostingRegressor() #context.model = tree.DecisionTreeRegressor log.info(context.model) context.asset = sid(24) algo.attach_pipeline( make_pipeline(), 'data_pipe' ) if context.optimize_portfolio: algo.attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline') algo.schedule_function( rebalance, date_rules.every_day(),#week_start(), time_rule=time_rules.market_open(hours=1,minutes=45) ) def train_model_once(context,data): if not context.trained: pipe_data = context.train_data X = pipe_data[context.input_fields].values y = np.array(pipe_data['returns'].values).reshape((-1,)) log.info("Now training!") context.model.fit(X,y) print(context.model.score(X,y)) pipe_data, context.train_data = "0", "0" context.trained = True def before_trading_start(context, data): context.pipeline_data = algo.pipeline_output('data_pipe') if context.optimize_portfolio: context.risk_loading_pipeline = algo.pipeline_output('risk_loading_pipeline') if not context.trained: context.assets = context.pipeline_data.index.values.tolist() context.train_data = get_historical_pipeline_data(context,data).dropna() train_model_once(context,data) def get_historical_pipeline_data(context,data): all_stock_results = [] log.info(len(context.assets)) all_stock_data = data.history(context.assets, fields=["price","open","high","low","volume"], bar_count=context.train_days_count, frequency="1d") for asset in context.assets: try: hist_data = all_stock_data.minor_xs(asset) hist_data["close"] = hist_data["price"] sma_10 = pd.DataFrame(hist_data["price"].rolling(10).mean()) sma_10["sma_10"] = sma_10['price'] sma_10 = sma_10.drop(["price"],axis=1) sma_30 = pd.DataFrame(hist_data["price"].rolling(30).mean()) sma_30["sma_30"] = sma_30["price"] sma_30 = sma_30.drop(["price"],axis=1) pcchange = pd.DataFrame(hist_data["price"].pct_change(periods=2)).shift(-2) pcchange["returns"] = pcchange["price"] pcchange = pcchange.drop(["price"],axis=1) hist_data = hist_data.drop(["price"],axis=1) res = pd.concat([hist_data, sma_10, sma_30,pcchange], axis=1) asset_col = [] for x in range(context.train_days_count): asset_col.append(asset) res = res.assign(asset=asset_col) all_stock_results.append(res) except TypeError as e: log.error(e) log.error(asset, "This Stock might not have sufficient historical data, to be suitable for training.") pipe_data = pd.concat(all_stock_results) pipe_data["date"] = pipe_data.index pipe_data = pipe_data.set_index('asset') return pipe_data def make_pipeline(): yesterday_close = USEquityPricing.close.latest yesterday_open = USEquityPricing.open.latest yesterday_high = USEquityPricing.high.latest yesterday_low = USEquityPricing.low.latest yesterday_volume = USEquityPricing.volume.latest sma_10 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10) sma_30 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30) returns = Returns(window_length=2) pipe = Pipeline( columns={ 'close': yesterday_close, 'open':yesterday_open, 'high': yesterday_high, 'low': yesterday_low, 'volume': yesterday_volume, 'sma_10':sma_10, 'sma_30':sma_30, 'returns':returns }, screen = QTradableStocksUS() ) return pipe def rebalance(context, data): X = context.pipeline_data[context.input_fields] print(X.shape) X = X.dropna().values print(len(X)) y = context.model.predict(X) alphas = pd.Series(data=y,index=context.pipeline_data.index.values)#.sort_values(ascending=True) objective = opt.MaximizeAlpha(alphas) sector_style_risk = opt.experimental.RiskModelExposure(risk_model_loadings=context.risk_loading_pipeline) max_leverage = opt.MaxGrossExposure(context.max_leverage) constrain_pos_size = opt.PositionConcentration.with_equal_bounds( -context.max_pos_size, context.max_pos_size ) dollar_neutral = opt.DollarNeutral(tolerance=0.000001) max_turnover = opt.MaxTurnover(context.max_turnover) constraints = [max_turnover,max_leverage,sector_style_risk,dollar_neutral] algo.order_optimal_portfolio(objective,constraints)