将quantopian的动量策略迁移到老虎证券量化api

原quantopian的动量策略，感谢原策略作者
首先需要获取数据
dataframe的列是各个股票的代码，index是时间，日频

def get_price(bars_num):stocks = read_sp_500code()df=pd.DataFrame(columns=['symbol','time','open','high','low','close','volume'])#每次取数据要取的条目数量items=math.floor(1000/bars_num)stock_num=len(stocks)print(stock_num)roundtrip=math.floor(stock_num/items)print(roundtrip)for i in range(roundtrip):bars = quote_client.get_bars(stocks[i*items:i*items+items],period=BarPeriod.DAY,begin_time=-1, end_time=-1,right=QuoteRight.BR,limit=bars_num)df=df.append(bars,ignore_index=True)print(bars)bars = quote_client.get_bars(stocks[roundtrip*items:stock_num],\period=BarPeriod.DAY,begin_time=-1,end_time=-1,right=QuoteRight.BR,limit=bars_num)df=df.append(bars,ignore_index=True)df.drop(['time','open','high','low','volume'],axis=1,inplace=True)print(df)return_df=pd.DataFrame(columns=stocks)for i in range(len(stocks)):close = df[i*bars_num:i*bars_num+bars_num]['close'].valuesprint(close)print(type(close))return_df[stocks[i]]=closeprint(return_df)return return_df

"""
Simple equity momentum model by Andreas Clenow
cloned from https://www.quantopian.com/posts/equity-momentum
Purpose:
To capture momentum effect in US stock markets.
Implements two methods of reducing downside.
Index level trend filter, and minimum momentum.Momentum analytic:
Uses annualized exponential regression slope, multiplied by R2, to adjust for fit.
Possibility to use average of two windows.Settings:
* Investment universe
* Momentum windows (x2)
* Index trend filter on/off
* Index trend filter window
* Minimum required slope
* Exclude recent x days
* Trading frequency
* Cash management via bond etf, on off
* Bond ETF selection
* Sizing method, inverse vola or equal.Suggested areas of research and improvements:
* Use pipeline to limit number of processed stocks, by putting the regression logic in there.
* Adding fundamental factors.
* Portfolio sizes
* Market cap weighting / inverse market cap weighting. (you may want to z-score and winsorize)
* Momentum analytic: There are many possible ways. Try simplifications and variations."""from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import AverageDollarVolume, CustomFactor, SimpleMovingAverage, Latest
from quantopian.pipeline.filters.morningstar import Q500US, Q1500US
from quantopian.pipeline.data import morningstarimport numpy as np  # we're using this for various math operations
from scipy import stats  # using this for the reg slope
import pandas as pddef slope(ts):"""Input: Price time series.Output: Annualized exponential regression slope, multipl"""x = np.arange(len(ts))log_ts = np.log(ts)#print('This is ts',ts)#print('This is log_ts:\n',log_ts)#print(' ')slope, intercept, r_value, p_value, std_err = stats.linregress(x, log_ts)#print(slope)annualized_slope = (np.power(np.exp(slope), 250) - 1) * 100#print('This is annualized_slope',annualized_slope)return annualized_slope * (r_value ** 2)def inv_vola_calc(ts):"""Input: Price time series.Output: Inverse exponential moving average standard deviation. Purpose: Provides inverse vola for use in vola parity position sizing."""returns = np.log(ts).diff()#print (type(returns))stddev = returns.ewm(halflife=20, ignore_na=True, min_periods=0,adjust=True).std(bias=False).dropna()return 1 / stddev.iloc[-1]def initialize(context):"""Setting our variables. Modify here to test differentiterations of the model."""# Investment Universe 1 = Q500US, 2 = Q1500UScontext.investment_set = 1# This version uses the average of two momentum slopes.# Want just one? Set them both to the same number.context.momentum_window = 60  # first momentum window.context.momentum_window2 = 90  # second momentum window# Limit minimum slope. Keep in mind that shorter momentum windows# yield more extreme slope numbers. Adjust one, and you may want# to adjust the other.context.minimum_momentum = 60  # momentum score cap# Fixed number of stocks in the portfolio. How diversified# do you want to be?context.number_of_stocks = 25  # portfolio sizecontext.index_id = sid(8554) # identifier for the SPY. used for trend filter.context.index_average_window = 100  # moving average periods for index filter# enable/disable trend filter.context.index_trend_filter = True  # Most momentum research excludes most recent data.context.exclude_days = 5  # excludes most recent days from momentum calculation# Set trading frequency here.context.trading_frequency = date_rules.month_start()# identifier for the cash management etf, if used.context.use_bond_etf = Truecontext.bond_etf = sid(23870) # 1 = inv.vola. 2 = equal size. Suggest to implement # market cap and inverse market cap as well. There's # lots of room for development here.context.size_method = 2 # Schedule rebalanceschedule_function(my_rebalance,context.trading_frequency,time_rules.market_open(hours=1))# Schedule daily recording of number of positions - For display in back# test results only.schedule_function(my_record_vars,date_rules.every_day(),time_rules.market_close())# Create our dynamic stock selector - getting the top 500 most liquid US# stocks.if(context.investment_set == 1):inv_set = Q500US()print(type(inv_set))elif(context.investment_set == 2):inv_set = Q1500US()attach_pipeline(make_pipeline(inv_set), 'investment_universe')def make_pipeline(investment_set): """This will return the selected stocks by market cap, dynamically updated."""# Base universe base_universe = investment_set yesterday_close = USEquityPricing.close.latestpipe = Pipeline(screen=base_universe,columns={'close': yesterday_close,})return pipedef my_rebalance(context, data):"""Our monthly rebalancing routine"""# First update the stock universe. context.output = pipeline_output('investment_universe')context.security_list = context.output.indexprint(context.security_list)print(context.security_list[0])print(type(context.security_list[0]))# Get datahist_window = max(context.momentum_window,context.momentum_window2) + context.exclude_dayshist = data.history(context.security_list, "close", hist_window, "1d")#hist_only = data.history('Equity(24 [AAPL])', "close", hist_window, "1d")#print(hist_only)#print(hist)data_end = -1 * context.exclude_days # exclude most recent datamomentum1_start = -1 * (context.momentum_window + context.exclude_days)momentum_hist1 = hist[momentum1_start:data_end]momentum2_start = -1 * (context.momentum_window2 + context.exclude_days)momentum_hist2 = hist[momentum2_start:data_end]# Calculate momentum scores for all stocks.momentum_list = momentum_hist1.apply(slope)  # Mom Window 1momentum_list2 = momentum_hist2.apply(slope)  # Mom Window 2#print(momentum_list)# Combine the lists and make averagemomentum_concat = pd.concat((momentum_list, momentum_list2))mom_by_row = momentum_concat.groupby(momentum_concat.index)mom_means = mom_by_row.mean()# Sort the momentum list, and we've got ourselves a ranking table.ranking_table = mom_means.sort_values(ascending=False)# Get the top X stocks, based on the setting above. Slice the dictionary.# These are the stocks we want to buy.buy_list = ranking_table[:context.number_of_stocks]final_buy_list = buy_list[buy_list > context.minimum_momentum] # those who passed minimum slope requirement# Calculate inverse volatility, for position size.inv_vola_table = hist[buy_list.index].apply(inv_vola_calc)# sum inv.vola for all selected stocks.sum_inv_vola = np.sum(inv_vola_table)# Check trend filter if enabled.if (context.index_trend_filter):index_history = data.history(context.index_id,"close",context.index_average_window,"1d")  # Gets index historyindex_sma = index_history.mean()  # Average of index historycurrent_index = index_history[-1]  # get last element# declare bull if index is over averagebull_market = current_index > index_sma# if trend filter is used, only buy in bull markets# else always buyif context.index_trend_filter:can_buy = bull_marketelse:can_buy = Trueequity_weight = 0.0 # for keeping track of exposure to stocks# Sell positions no longer wanted.for security in context.portfolio.positions:if (security not in final_buy_list):if (security.sid != context.bond_etf):# print 'selling %s' % securityorder_target(security, 0.0)vola_target_weights = inv_vola_table / sum_inv_volafor security in final_buy_list.index:# allow rebalancing of existing, and new buys if can_buy, i.e. passed trend filter.if (security in context.portfolio.positions) or (can_buy): if (context.size_method == 1):weight = vola_target_weights[security]elif (context.size_method == 2):weight = (1.0 / context.number_of_stocks)print context.number_of_stocksorder_target_percent(security, weight)equity_weight += weight# Fill remaining portfolio with bond ETFetf_weight = max(1 - equity_weight, 0.0)print 'equity exposure should be %s ' % equity_weightif (context.use_bond_etf):order_target_percent(context.bond_etf, etf_weight)def my_record_vars(context, data):"""This routine just records number of open positions and exposure levelfor display purposes."""etf_exp = 0.0pos_count = 0eq_exp = 0.0for position in context.portfolio.positions.itervalues():pos_count += 1if (position.sid == context.bond_etf):etf_exp += (position.amount * position.last_sale_price) / \context.portfolio.portfolio_valueelse:eq_exp += (position.amount * position.last_sale_price) / \context.portfolio.portfolio_valuerecord(equity_exposure=eq_exp)record(bond_exposure=etf_exp)record(tot_exposure=context.account.leverage)record(positions=pos_count)

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