Playing with the ODSC example to mix several rankings in combination.

Clone Algorithm

50

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Backtest from
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Cumulative performance:

Algorithm
Benchmark

Custom data:

Total Returns

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Alpha

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Beta

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Sharpe

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Sortino

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Max Drawdown

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Benchmark Returns

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Volatility

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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 |

""" This algorithm demonstrates the concept of long-short equity. It uses two fundamental factors to rank all equities. It then longs the top of the ranking and shorts the bottom. For information on long-short equity strategies, please see the corresponding lecture on https://www.quantopian.com/lectures The dollar volume threshold is in place because orders of thinly traded securities can fail to fill in time and result in worse pricing and returns. WARNING: These factors were selected because they worked in the past over the specific time period we choose. We do not anticipate them working in the future. In practice finding your own factors is the hardest part of developing any long-short equity strategy. This algorithm is meant to serve as a framework for testing your own ranking factors. This algorithm was developed as part of Quantopian's Lecture Series. Please direct any questions, feedback, or corrections to [email protected] """ from quantopian.algorithm import attach_pipeline, pipeline_output from quantopian.pipeline import Pipeline from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage from quantopian.pipeline.data.builtin import USEquityPricing from quantopian.pipeline.data import morningstar import numpy as np import pandas as pd class Value(CustomFactor): inputs = [morningstar.income_statement.ebit, morningstar.valuation.enterprise_value] window_length = 1 def compute(self, today, assets, out, ebit, ev): out[:] = ebit[-1] / ev[-1] class Quality(CustomFactor): # Pre-declare inputs and window_length inputs = [morningstar.operation_ratios.roe,] window_length = 1 def compute(self, today, assets, out, roe): out[:] = roe[-1] class AvgDailyDollarVolumeTraded(CustomFactor): inputs = [USEquityPricing.close, USEquityPricing.volume] def compute(self, today, assets, out, close_price, volume): out[:] = np.mean(close_price * volume, axis=0) class Momentum(CustomFactor): inputs = [USEquityPricing.close] window_length = 200 def compute(self, today, assets, out, close_price): out[:] = close_price[-1]/close_price[-len(close_price)] def make_pipeline(): """ Create and return our pipeline. We break this piece of logic out into its own function to make it easier to test and modify in isolation. In particular, this function can be copy/pasted into research and run by itself. """ pipe = Pipeline() # Basic value and quality metrics. value = Value() pipe.add(value, "value") quality = Quality() pipe.add(quality, "quality") momentum = Momentum() pipe.add(momentum, "momentum") # We only want to trade relatively liquid stocks. # Build a filter that only passes stocks that have $10,000,000 average # daily dollar volume over the last 20 days. dollar_volume = AvgDailyDollarVolumeTraded(window_length=20) is_liquid = (dollar_volume > 1e7) # We also don't want to trade penny stocks, which we define as any stock with an # average price of less than $5.00 over the last 200 days. sma_200 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=200) not_a_penny_stock = (sma_200 > 5) # Before we do any other ranking, we want to throw away these assets. initial_screen = (is_liquid & not_a_penny_stock) # Construct and add a Factor representing the average rank of each asset by our # value and quality metrics. # By applying a mask to the rank computations, we remove any stocks that failed # to meet our initial criteria **before** computing ranks. This means that the # stock with rank 10.0 is the 10th-lowest stock that passed `initial_screen`. combined_rank = ( value.rank(mask=initial_screen) + quality.rank(mask=initial_screen) + momentum.rank(mask = initial_screen) ) pipe.add(combined_rank, 'combined_rank') # Build Filters representing the top and bottom 200 stocksby # our combined ranking system. # We'll use these as our tradeable universe each day. longs = combined_rank.top(200) shorts = combined_rank.bottom(200) # The final output of our pipeline should only include # the top/bottom 200 stocks by our criteria. pipe.set_screen(longs | shorts) pipe.add(longs, 'longs') pipe.add(shorts, 'shorts') return pipe def initialize(context): # Set slippage and commission to zero to evaulate the signal generating # ability of the algorithm set_commission(commission.PerShare(cost=0.0075, min_trade_cost=1.0)) set_slippage(slippage.VolumeShareSlippage(volume_limit=0.025, price_impact=0.1)) context.long_leverage = 0.50 context.short_leverage = -0.50 context.spy = sid(8554) attach_pipeline(make_pipeline(), 'ranking_example') # Used to avoid purchasing any leveraged ETFs context.dont_buys = security_lists.leveraged_etf_list # Schedule my rebalance function schedule_function(func=rebalance, date_rule=date_rules.month_start(days_offset=0), time_rule=time_rules.market_open(hours=0,minutes=30), half_days=True) # Schedule a function to plot leverage and position count schedule_function(func=record_vars, date_rule=date_rules.every_day(), time_rule=time_rules.market_close(), half_days=True) def before_trading_start(context, data): # Call pipeline_output to get the output # Note this is a dataframe where the index is the SIDs for all # securities to pass my screen and the columns are the factors which output = pipeline_output('ranking_example') ranks = output['combined_rank'] long_ranks = ranks[output['longs']] short_ranks = ranks[output['shorts']] context.long_weights = (long_ranks / long_ranks.sum()) log.info("Long Weights:") log.info(context.long_weights) context.short_weights = (short_ranks / short_ranks.sum()) log.info("Short Weights:") log.info(context.short_weights) context.active_portfolio = context.long_weights.index.union(context.short_weights.index) def record_vars(context, data): # Record and plot the leverage, number of positions, and expsoure of our portfolio over time. record(num_positions=len(context.portfolio.positions), exposure=context.account.net_leverage, leverage=context.account.leverage) # This function is scheduled to run at the start of each month. def rebalance(context, data): """ Allocate our long/short portfolio based on the weights supplied by context.long_weights and context.short_weights. """ # Order our longs. log.info("ordering longs") for long_stock, long_weight in context.long_weights.iterkv(): if data.can_trade(long_stock): if get_open_orders(long_stock): continue if long_stock in context.dont_buys: continue order_target_percent(long_stock, context.long_leverage * long_weight) # Order our shorts. log.info("ordering shorts") for short_stock, short_weight in context.short_weights.iterkv(): if data.can_trade(short_stock): if get_open_orders(short_stock): continue if short_stock in context.dont_buys: continue order_target_percent(short_stock, context.short_leverage * short_weight) # Sell any positions in assets that are no longer in our target portfolio. for security in context.portfolio.positions: if get_open_orders(security): continue if data.can_trade(security): # Work around inability to sell de-listed stocks. if security not in context.active_portfolio: order_target_percent(security, 0)