I really need to add a provision whereby only "winners" are rebalanced not losers. But there you go. Many people here can't seem to get a handle on simple trend following systems so I thought this might help!

Clone Algorithm

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Backtest from
to
with
initial capital

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 |

from quantopian.algorithm import attach_pipeline, pipeline_output from quantopian.pipeline import Pipeline from quantopian.pipeline import CustomFactor from quantopian.pipeline.data.builtin import USEquityPricing from quantopian.pipeline.data import morningstar import numpy as np from collections import defaultdict class momentum_factor_1(CustomFactor): inputs = [USEquityPricing.close] window_length = 20 def compute(self, today, assets, out, close): out[:] = close[-1]/close[0] class momentum_factor_2(CustomFactor): inputs = [USEquityPricing.close] window_length = 60 def compute(self, today, assets, out, close): out[:] = close[-1]/close[0] class momentum_factor_3(CustomFactor): inputs = [USEquityPricing.close] window_length = 125 def compute(self, today, assets, out, close): out[:] = close[-1]/close[0] class momentum_factor_4(CustomFactor): inputs = [USEquityPricing.close] window_length = 252 def compute(self, today, assets, out, close): out[:] = close[-1]/close[0] class market_cap(CustomFactor): inputs = [USEquityPricing.close, morningstar.valuation.shares_outstanding] window_length = 1 def compute(self, today, assets, out, close, shares): out[:] = close[-1] * shares[-1] class efficiency_ratio(CustomFactor): inputs = [USEquityPricing.close, USEquityPricing.high, USEquityPricing.low] window_length = 252 def compute(self, today, assets, out, close, high, low): lb = self.window_length e_r = np.zeros(len(assets), dtype=np.float64) a=np.array(([high[1:(lb):1]-low[1:(lb):1],abs(high[1:(lb):1]-close[0:(lb-1):1]),abs(low[1:(lb):1]-close[0:(lb-1):1])])) b=a.T.max(axis=1) c=b.sum(axis=1) e_r=abs(close[-1]-close[0]) /c out[:] = e_r def initialize(context): set_commission(commission.PerShare(cost=0.005, min_trade_cost=1.00)) schedule_function(func=rebalance, date_rule=date_rules.month_start(days_offset=5), time_rule=time_rules.market_open(), half_days=True) schedule_function(close_orders,date_rule=date_rules.week_end(),time_rule=time_rules.market_close()) set_do_not_order_list(security_lists.leveraged_etf_list) context.acc_leverage = 1.00 context.holdings =10 context.profit_taking_factor = 0.01 context.profit_target={} context.profit_taken={} context.entry_date={} context.stop_pct = 0.75 context.stop_price = defaultdict(lambda:0) pipe = Pipeline() attach_pipeline(pipe, 'ranked_stocks') factor1 = momentum_factor_1() pipe.add(factor1, 'factor_1') factor2 = momentum_factor_2() pipe.add(factor2, 'factor_2') factor3 = momentum_factor_3() pipe.add(factor3, 'factor_3') factor4 = momentum_factor_4() pipe.add(factor4, 'factor_4') factor5=efficiency_ratio() pipe.add(factor5, 'factor_5') mkt_screen = market_cap() stocks = mkt_screen.top(3000) factor_5_filter = factor5 > 0.031 total_filter = (stocks& factor_5_filter) pipe.set_screen(total_filter) factor1_rank = factor1.rank(mask=total_filter, ascending=False) pipe.add(factor1_rank, 'f1_rank') factor2_rank = factor2.rank(mask=total_filter, ascending=False) pipe.add(factor2_rank, 'f2_rank') factor3_rank = factor3.rank(mask=total_filter, ascending=False) pipe.add(factor3_rank, 'f3_rank') factor4_rank = factor4.rank(mask=total_filter, ascending=False) pipe.add(factor4_rank, 'f4_rank') combo_raw = (factor1_rank+factor2_rank+factor3_rank+factor4_rank)/4 pipe.add(combo_raw, 'combo_raw') pipe.add(combo_raw.rank(mask=total_filter), 'combo_rank') def before_trading_start(context, data): context.output = pipeline_output('ranked_stocks') ranked_stocks = context.output.fillna(0) ranked_stocks = context.output[context.output.factor_1 > 0] ranked_stocks = context.output[context.output.factor_2 > 0] ranked_stocks = context.output[context.output.factor_3 > 0] ranked_stocks = context.output[context.output.factor_4 > 0] ranked_stocks = context.output[context.output.factor_5 > 0] context.stock_list = ranked_stocks.sort(['combo_rank'], ascending=True).iloc[:context.holdings] update_universe(context.stock_list.index) def handle_data(context, data): for stock in context.portfolio.positions: price = data[stock].price context.stop_price[stock] = max(context.stop_price[stock], context.stop_pct * price) for stock in context.portfolio.positions: if data[stock].price < context.stop_price[stock]: order_target(stock, 0) context.stop_price[stock] = 0 current_date = get_datetime() record(leverage=context.account.leverage, positions=len(context.portfolio.positions)) for stock in context.portfolio.positions: if (stock.end_date - current_date).days < 2: order_target_percent(stock, 0.0) print "Long List" log.info("\n" + str(context.stock_list.sort(['combo_rank'], ascending=True).head(context.holdings))) if data[stock].close_price > context.profit_target[stock]: context.profit_target[stock] = data[stock].close_price*1.25 profit_taking_amount = context.portfolio.positions[stock].amount * context.profit_taking_factor order_target(stock, profit_taking_amount) def rebalance(context,data): weight = context.acc_leverage / len(context.stock_list) for stock in context.stock_list.index: if stock in data: if context.stock_list.factor_1[stock]>1: if (stock.end_date - get_datetime()).days > 35: if stock not in security_lists.leveraged_etf_list: order_target_percent(stock, weight) context.profit_target[stock] = data[stock].close_price*1.25 for stock in context.portfolio.positions.iterkeys(): if stock not in context.stock_list.index or context.stock_list.factor_1[stock]<=1: order_target(stock, 0) def close_orders(context, data): orders = get_open_orders() if orders: for o in orders: cancel_order(o)

We have migrated this algorithm to work with a new version of the Quantopian API. The code is different than the original version, but the investment rationale of the algorithm has not changed. We've put everything you need to know here on one page.