Just curious if this algo would make any sense at all in the conceived Quantopian crowd-sourced hedge fund?

If anyone in the crowd has an opinion, or if someone at Q has time to dig into it, I would be glad to get a critique (including capacity at higher levels of capital). Personally, I think it is a goof-ball idea, but maybe not?

And if you are looking to enter the contest with a few clicks, I think this algo will make it past the various checks (although you might want to fiddle with it a bit to see if it can be improved. Sharpe is low due to volatility? Trades daily, but maybe commissions eat into returns?).

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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 |

# Adapted from: # Li, Bin, and Steven HOI. "On-Line Portfolio Selection with Moving Average Reversion." The 29th International Conference on Machine Learning (ICML2012), 2012. # http://icml.cc/2012/papers/168.pdf import numpy as np from scipy import optimize import pandas as pd def initialize(context): context.eps = 1.005 context.pct_index = 0.5 # percentage short QQQ context.leverage = 2.5 print 'context.eps = ' + str(context.eps) print 'context.pct_index = ' + str(context.pct_index) print 'context.leverage = ' + str(context.leverage) schedule_function(trade, date_rules.every_day(), time_rules.market_open(minutes=60)) def before_trading_start(context,data): fundamental_df = get_fundamentals( query( fundamentals.valuation.market_cap, ) .filter(fundamentals.company_reference.primary_exchange_id == 'NAS') .filter(fundamentals.valuation.market_cap != None) .order_by(fundamentals.valuation.market_cap.desc()).limit(30)) update_universe(fundamental_df.columns.values) context.stocks = [stock for stock in fundamental_df] # check if data exists for stock in context.stocks: if stock not in data: context.stocks.remove(stock) def handle_data(context, data): record(leverage = context.account.leverage) def get_allocation(context,data,n,prices): prices = pd.ewma(prices,span=390).as_matrix(context.stocks) b_t = [] for stock in context.stocks: b_t.append(context.portfolio.positions[stock].amount*data[stock].price) m = len(b_t) b_0 = np.zeros(m) denom = np.sum(b_t) if denom == 0.0: b_t = np.copy(b_0) else: b_t = np.divide(b_t,denom) x_tilde = [] for i, stock in enumerate(context.stocks): mean_price = np.mean(prices[:,i]) x_tilde.append(mean_price/prices[-1,i]) bnds = [] limits = [0,1] for stock in context.stocks: bnds.append(limits) bnds = tuple(tuple(x) for x in bnds) cons = ({'type': 'eq', 'fun': lambda x: np.sum(x)-1.0}, {'type': 'ineq', 'fun': lambda x: np.dot(x,x_tilde) - context.eps}) res= optimize.minimize(norm_squared, b_0, args=b_t,jac=norm_squared_deriv,method='SLSQP',constraints=cons,bounds=bnds, options={'disp': False, 'maxiter': 100, 'iprint': 1, 'ftol': 1e-6}) allocation = res.x allocation[allocation<0] = 0 allocation = allocation/np.sum(allocation) if res.success and (np.dot(allocation,x_tilde)-context.eps > 0): return (allocation,np.dot(allocation,x_tilde)) else: return (b_t,1) def trade(context,data): # check if data exists for stock in context.stocks: if stock not in data: context.stocks.remove(stock) # check for de-listed stocks & leveraged ETFs for stock in context.stocks: if stock.security_end_date < get_datetime(): # de-listed ? context.stocks.remove(stock) if stock in security_lists.leveraged_etf_list: # leveraged ETF? context.stocks.remove(stock) # check for open orders if get_open_orders(): return # find average weighted allocation over range of trailing window lengths a = np.zeros(len(context.stocks)) w = 0 prices = history(8*390,'1m','price') for n in range(1,9): (a,w) = get_allocation(context,data,n,prices.tail(n*390)) a += w*a w += w allocation = a/w allocation = allocation/np.sum(allocation) allocate(context,data,allocation) def allocate(context, data, desired_port): # order long stocks long_pct = 1.0 - context.pct_index for i, stock in enumerate(context.stocks): order_target_percent(stock, long_pct*context.leverage*desired_port[i]) qqq = sid(19920) # QQQ # short index order_target_percent(qqq,-context.leverage*context.pct_index) for stock in data: if stock in context.stocks: pass elif stock == qqq: pass else: order_target_percent(stock,0) def norm_squared(b,*args): b_t = np.asarray(args) delta_b = b - b_t return 0.5*np.dot(delta_b,delta_b.T) def norm_squared_deriv(b,*args): b_t = np.asarray(args) delta_b = b - b_t return delta_b

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.