The overnight gap strategy (mean reversion of the overnight price gap) is well known and in this example I wanted to quantify the alpha associated with it. The alpha is huge but is that easy to trade it?

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Researching intraday factors with Alphalens: overnight price gap example

The overnight gap strategy (mean reversion of the overnight price gap) is well known and in this example I wanted to quantify the alpha associated with it. The alpha is huge but is that easy to trade it?

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4 responses

Note that I used FixedSlippage, so there is no volume limitation in the orders. This is just for fun, I wanted to see huge numbers.

Clone Algorithm

50

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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 quantopian.algorithm import attach_pipeline, pipeline_output from quantopian.pipeline import Pipeline from quantopian.pipeline.data.builtin import USEquityPricing from quantopian.pipeline.data import morningstar from quantopian.pipeline import factors, filters, classifiers from quantopian.pipeline.filters import StaticAssets, QTradableStocksUS from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage, AverageDollarVolume, Returns import pandas as pd import numpy as np import scipy.stats as stats def make_pipeline(context): universe = QTradableStocksUS() pipe = Pipeline() pipe.set_screen(universe) pipe.add(universe, "universe") return pipe def initialize(context): set_commission(commission.PerShare(cost=0.00, min_trade_cost=0)) set_slippage(slippage.FixedSlippage(spread=0.00)) attach_pipeline(make_pipeline(context), 'factors') schedule_function(rebalance, date_rules.every_day(), time_rules.market_open()) schedule_function(close_all, date_rules.every_day(), time_rules.market_close(minutes=270)) #schedule_function(my_record, date_rules.every_day(), time_rules.market_close()) def my_record(context, data): record(lever=context.account.leverage, exposure=context.account.net_leverage, num_pos=len(context.portfolio.positions)) def get_weights(pipe_out, rank_cols, max_long_sec, max_short_sec, group_neutral): if group_neutral: pipe_out = pipe_out[rank_cols + ['group']] else: pipe_out = pipe_out[rank_cols] pipe_out = pipe_out.replace([np.inf, -np.inf], np.nan) pipe_out = pipe_out.dropna() def to_weights(factor, is_long_short): if is_long_short: demeaned_vals = factor - factor.mean() return demeaned_vals / demeaned_vals.abs().sum() else: return factor / factor.abs().sum() # # rank stocks so that we can select long/short ones # weights = pd.Series(0., index=pipe_out.index) for rank_col in rank_cols: if not group_neutral: # rank regardless of sector code weights += to_weights(pipe_out[rank_col], True) else: # weight each sector equally weights += pipe_out.groupby(['group'])[rank_col].apply(to_weights, True) if not group_neutral: # rank regardless of sector/group code longs = weights[ weights > 0 ] shorts = weights[ weights < 0 ].abs() if max_long_sec: longs = longs.order(ascending=False).head(max_long_sec) if max_short_sec: shorts = shorts.order(ascending=False).head(max_short_sec) else: # weight each group/sector equally sectors = pipe_out['group'].unique() num_sectors = len(sectors) longs = pd.Series() shorts = pd.Series() for current_sector in sectors: _w = weights[ pipe_out['group'] == current_sector ] _longs = _w[ _w > 0 ] _shorts = _w[ _w < 0 ].abs() if max_long_sec: _longs = _longs.order(ascending=False).head(max_long_sec/num_sectors) if max_short_sec: _shorts = _shorts.order(ascending=False).head(max_short_sec/num_sectors) _longs /= _longs.sum() _shorts /= _shorts.sum() longs = longs.append( _longs ) shorts = shorts.append( _shorts ) longs = longs[ longs > 0 ] shorts = shorts[ shorts > 0 ] longs /= longs.sum() shorts /= shorts.sum() return longs, shorts # Compute final rank and assign long and short baskets. def before_trading_start(context, data): results = pipeline_output('factors') print 'Basket of stocks %d' % len(results) context.universe = results.index def rebalance(context, data): prices = data.history(context.universe, 'price', 2, '1m') # compute gap factor factor = prices.iloc[:2].pct_change().iloc[1,:] # yesterday close to today open gap factor = -factor # mean reverting factor.name = 'gap' factor = factor.to_frame() context.longs, context.shorts = get_weights(factor, ['gap'], max_long_sec=150, max_short_sec=150, group_neutral=False) context.longs /= 2 context.shorts /= 2 print 'longs weighted (length %d, sum %f):\n' % (len(context.longs.index), context.longs.sum()), context.longs.head(3), context.longs.tail(3) print 'shorts weighted (length %d, sum %f):\n' % (len(context.shorts.index), context.shorts.sum()), context.shorts.head(3), context.shorts.tail(3) for security in context.shorts.index: if get_open_orders(security): continue if data.can_trade(security): order_target_percent(security, -context.shorts[security]) for security in context.longs.index: if get_open_orders(security): continue if data.can_trade(security): order_target_percent(security, context.longs[security]) for security in context.portfolio.positions: if get_open_orders(security): continue if data.can_trade(security) and security not in (context.longs.index | context.shorts.index): order_target_percent(security, 0) def close_all(context, data): my_record(context, data) os = get_open_orders() for ol in os.values(): for o in ol: cancel_order(o) for sid in context.portfolio.positions: order_target(sid, 0)

@Luca,

Thanks for posting this...always interesting to see your work!

Couldn't resist mucking with this a bit...didn't actually change much, if anything, except for running it for the past two years, which is my pavlovian version of Q-normalization!

Here is my take:

GOOD NEWS:

Fantastic results as measured by cumulative returns!...>49%/2 years

BAD NEWS:

Pretty much ZERO risk factors, except for ~200% turnover each day!

Hmmm. why is this bad you might ask...well bad for me in that now I don't trust that this simulator(backtester) is set up at all to handle intraday trading,

hence, I don't trust the GOOD NEWS of high cumulative returns without a lot more work and validation, especially in the commisions&slippage areas.

I suppose that High Frequency Traders have figured all this out, yet I don't currently trust Zipline in the intraday sphere.

Love to be wrong here...as I really like those returns!

alan

Clone Algorithm

27

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 quantopian.algorithm import attach_pipeline, pipeline_output from quantopian.pipeline import Pipeline from quantopian.pipeline.data.builtin import USEquityPricing from quantopian.pipeline.data import morningstar from quantopian.pipeline import factors, filters, classifiers from quantopian.pipeline.filters import StaticAssets, QTradableStocksUS from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage, AverageDollarVolume, Returns import pandas as pd import numpy as np import scipy.stats as stats def make_pipeline(context): universe = QTradableStocksUS() pipe = Pipeline() pipe.set_screen(universe) pipe.add(universe, "universe") return pipe def initialize(context): set_commission(commission.PerShare(cost=0.00, min_trade_cost=0)) set_slippage(slippage.FixedSlippage(spread=0.00)) attach_pipeline(make_pipeline(context), 'factors') schedule_function(rebalance, date_rules.every_day(), time_rules.market_open()) schedule_function(close_all, date_rules.every_day(), time_rules.market_close(minutes=270)) #schedule_function(my_record, date_rules.every_day(), time_rules.market_close()) def my_record(context, data): record(lever=context.account.leverage, exposure=context.account.net_leverage, num_pos=len(context.portfolio.positions)) def get_weights(pipe_out, rank_cols, max_long_sec, max_short_sec, group_neutral): if group_neutral: pipe_out = pipe_out[rank_cols + ['group']] else: pipe_out = pipe_out[rank_cols] pipe_out = pipe_out.replace([np.inf, -np.inf], np.nan) pipe_out = pipe_out.dropna() def to_weights(factor, is_long_short): if is_long_short: demeaned_vals = factor - factor.mean() return demeaned_vals / demeaned_vals.abs().sum() else: return factor / factor.abs().sum() # # rank stocks so that we can select long/short ones # weights = pd.Series(0., index=pipe_out.index) for rank_col in rank_cols: if not group_neutral: # rank regardless of sector code weights += to_weights(pipe_out[rank_col], True) else: # weight each sector equally weights += pipe_out.groupby(['group'])[rank_col].apply(to_weights, True) if not group_neutral: # rank regardless of sector/group code longs = weights[ weights > 0 ] shorts = weights[ weights < 0 ].abs() if max_long_sec: longs = longs.order(ascending=False).head(max_long_sec) if max_short_sec: shorts = shorts.order(ascending=False).head(max_short_sec) else: # weight each group/sector equally sectors = pipe_out['group'].unique() num_sectors = len(sectors) longs = pd.Series() shorts = pd.Series() for current_sector in sectors: _w = weights[ pipe_out['group'] == current_sector ] _longs = _w[ _w > 0 ] _shorts = _w[ _w < 0 ].abs() if max_long_sec: _longs = _longs.order(ascending=False).head(max_long_sec/num_sectors) if max_short_sec: _shorts = _shorts.order(ascending=False).head(max_short_sec/num_sectors) _longs /= _longs.sum() _shorts /= _shorts.sum() longs = longs.append( _longs ) shorts = shorts.append( _shorts ) longs = longs[ longs > 0 ] shorts = shorts[ shorts > 0 ] longs /= longs.sum() shorts /= shorts.sum() return longs, shorts # Compute final rank and assign long and short baskets. def before_trading_start(context, data): results = pipeline_output('factors') print 'Basket of stocks %d' % len(results) context.universe = results.index def rebalance(context, data): prices = data.history(context.universe, 'price', 2, '1m') # compute gap factor factor = prices.iloc[:2].pct_change().iloc[1,:] # yesterday close to today open gap factor = -factor # mean reverting factor.name = 'gap' factor = factor.to_frame() context.longs, context.shorts = get_weights(factor, ['gap'], max_long_sec=100, max_short_sec=100, group_neutral=False) #False context.longs /= 2 context.shorts /= 2 print 'longs weighted (length %d, sum %f):\n' % (len(context.longs.index), context.longs.sum()), context.longs.head(3), context.longs.tail(3) print 'shorts weighted (length %d, sum %f):\n' % (len(context.shorts.index), context.shorts.sum()), context.shorts.head(3), context.shorts.tail(3) for security in context.shorts.index: if get_open_orders(security): continue if data.can_trade(security): order_target_percent(security, -context.shorts[security]) for security in context.longs.index: if get_open_orders(security): continue if data.can_trade(security): order_target_percent(security, context.longs[security]) for security in context.portfolio.positions: if get_open_orders(security): continue if data.can_trade(security) and security not in (context.longs.index | context.shorts.index): order_target_percent(security, 0) def close_all(context, data): my_record(context, data) os = get_open_orders() for ol in os.values(): for o in ol: cancel_order(o) for sid in context.portfolio.positions: order_target(sid, 0)

I also really appreciate Luca’s work and for sharing it. However, I also don’t trust these numbers. Too good for such a simple strategy. I don’t think Zipline was really designed with intraday trading in mind, and I believe the risk model doesn’t work unless you have overnight positions.

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In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.