%matplotlib inline
import rnr_meucci_functions as rnr
import numpy as np
from zipline.api import (set_slippage, slippage, set_commission, commission, 
 order_target_percent, record, schedule_function,
 date_rules, time_rules, get_datetime, symbol)
# Set tickers for data loading i.e. DJIA constituents and DIA ETF for benchmark
tickers = ['MMM','AXP','AAPL','BA','CAT','CVX','CSCO','KO','DD','XOM','GE','GS',
 'HD','INTC','IBM','JNJ','JPM','MCD','MRK','MSFT','NKE','PFE','PG',
 'TRV','UNH','UTX','VZ','V','WMT','DIS', 'DIA']
# Set investable asset tickers
asset_tickers = ['MMM','AXP','AAPL','BA','CAT','CVX','CSCO','KO','DD','XOM','GE',
 'GS','HD','INTC','IBM','JNJ','JPM','MCD','MRK','MSFT','NKE','PFE','PG',
 'TRV','UNH','UTX','VZ','V','WMT','DIS']
 
def initialize(context):
 # Turn off the slippage model
 set_slippage(slippage.FixedSlippage(spread=0.0))
 # Set the commission model
 set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.0))
 context.day = -1 # using zero-based counter for days
 context.set_benchmark(symbol('DIA'))
 context.assets = []
 print('Setup investable assets...')
 for ticker in asset_tickers:
 #print(ticker)
 context.assets.append(symbol(ticker))
 context.n_asset = len(context.assets)
 context.n_portfolio = 40 # num mean-variance efficient portfolios to compute
 context.today = None
 context.tau = None
 context.min_data_window = 756 # min of 3 yrs data for calculations
 context.first_rebal_date = None
 context.first_rebal_idx = None
 context.weights = None
 # Schedule dynamic allocation calcs to occur 1 day before month end - note that
 # actual trading will occur on the close on the last trading day of the month
 schedule_function(rebalance,
 date_rule=date_rules.month_end(days_offset=1),
 time_rule=time_rules.market_close())
 # Record some stuff every day
 schedule_function(record_vars,
 date_rule=date_rules.every_day(),
 time_rule=time_rules.market_close())
def handle_data(context, data):
 context.day += 1
 #print(context.day)
 
def rebalance(context, data):
 # Wait for 756 trading days (3 yrs) of historical prices before trading
 if context.day < context.min_data_window - 1:
 return
 # Get expanding window of past prices and compute returns
 context.today = get_datetime().date() 
 prices = data.history(context.assets, "price", context.day, "1d")
 if context.first_rebal_date is None:
 context.first_rebal_date = context.today
 context.first_rebal_idx = context.day
 print('Starting dynamic allocation simulation...')
 # Get investment horizon in days ie number of trading days next month
 context.tau = rnr.get_num_days_nxt_month(context.today.month, context.today.year)
 # Calculate HFP distribution
 asset_rets = np.array(prices.pct_change(context.tau).iloc[context.tau:, :])
 num_scenarios = len(asset_rets)
 # Set Flexible Probabilities Using Exponential Smoothing
 half_life_prjn = 252 * 2 # in days
 lambda_prjn = np.log(2) / half_life_prjn
 probs_prjn = np.exp(-lambda_prjn * (np.arange(0, num_scenarios)[::-1]))
 probs_prjn = probs_prjn / sum(probs_prjn)
 mu_pc, sigma2_pc = rnr.fp_mean_cov(asset_rets.T, probs_prjn)
 # Perform shrinkage to mitigate estimation risk
 mu_shrk, sigma2_shrk = rnr.simple_shrinkage(mu_pc, sigma2_pc)
 weights, _, _ = rnr.efficient_frontier_qp_rets(context.n_portfolio, 
 sigma2_shrk, mu_shrk)
 print('Optimal weights calculated 1 day before month end on %s (day=%s)' \
 % (context.today, context.day))
 #print(weights)
 min_var_weights = weights[0,:]
 # Rebalance portfolio accordingly
 for stock, weight in zip(prices.columns, min_var_weights):
 order_target_percent(stock, np.asscalar(weight))
 context.weights = min_var_weights
 
def record_vars(context, data):
 record(weights=context.weights, tau=context.tau)
 
def analyze(perf, bm_value, start_idx):
 pd.DataFrame({'portfolio':results.portfolio_value,'benchmark':bm_value})\
 .iloc[start_idx:,:].plot(title='Portfolio Performance vs Benchmark',\
 figsize=(10, 8))
if __name__ == '__main__':
 from datetime import datetime
 import pytz
 from zipline.algorithm import TradingAlgorithm
 from zipline.utils.factory import load_bars_from_yahoo
 import pandas as pd
 import matplotlib.pyplot as plt
 
 # Create and run the algorithm.
 algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
 start = datetime(2010, 5, 1, 0, 0, 0, 0, pytz.utc)
 end = datetime(2016, 5, 31, 0, 0, 0, 0, pytz.utc)
 print('Getting Yahoo data for 30 DJIA stocks and DIA ETF as benchmark...')
 data = load_bars_from_yahoo(stocks=tickers, start=start, end=end)
 # Check price data
 data.loc[:, :, 'price'].plot(figsize=(8,7), title='Input Price Data')
 plt.ylabel('price in $');
 plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
 plt.show()
 
 # Run algorithm
 results = algo.run(data)
 
 # Fix possible issue with timezone
 results.index = results.index.normalize()
 if results.index.tzinfo is None:
 results.index = results.index.tz_localize('UTC')
 
 # Adjust benchmark returns for delayed trading due to 3 year min data window 
 bm_rets = algo.perf_tracker.all_benchmark_returns
 bm_rets[0:algo.first_rebal_idx + 2] = 0
 bm_rets.name = 'DIA'
 bm_rets.index.freq = None
 bm_value = algo.capital_base * np.cumprod(1+bm_rets)
 
 # Plot portfolio and benchmark values
 analyze(results, bm_value, algo.first_rebal_idx + 1)
 print('End value portfolio = {:.0f}'.format(results.portfolio_value.ix[-1]))
 print('End value benchmark = {:.0f}'.format(bm_value[-1]))
 
 # Plot end weights
 pd.DataFrame(results.weights.ix[-1], index=asset_tickers, columns=['w'])\
 .sort_values('w', ascending=False).plot(kind='bar', \
 title='End Simulation Weights', legend=None, figsize=(10, 8));

Getting Yahoo data for 30 DJIA stocks and DIA ETF as benchmark...

C:\Anaconda3\envs\py34\lib\site-packages\ipykernel\__main__.py:106: DeprecationWarning:
 load_bars_from_yahoo is deprecated, please register a yahoo_equities data bundle instead

Setup investable assets...
Starting dynamic allocation simulation...
Optimal weights calculated 1 day before month end on 2013年05月30日 (day=774)
Optimal weights calculated 1 day before month end on 2013年06月27日 (day=794)
Optimal weights calculated 1 day before month end on 2013年07月30日 (day=816)
.
.
.
Optimal weights calculated 1 day before month end on 2016年03月30日 (day=1487)
Optimal weights calculated 1 day before month end on 2016年04月28日 (day=1508)
Optimal weights calculated 1 day before month end on 2016年05月27日 (day=1529)
End value portfolio = 130872
End value benchmark = 125771

# Sequel Step - Ex-post performance analysis
import pyfolio as pf
returns, positions, transactions, gross_lev = pf.utils.\
 extract_rets_pos_txn_from_zipline(results)
trade_start = results.index[algo.first_rebal_idx + 1]
trade_end = datetime(2016, 5, 31, 0, 0, 0, 0, pytz.utc)
print('Annualised volatility of the portfolio = {:.4}'.\
 format(pf.timeseries.annual_volatility(returns[trade_start:trade_end])))
print('Annualised volatility of the benchmark = {:.4}'.\
 format(pf.timeseries.annual_volatility(bm_rets[trade_start:trade_end])))
print('')
pf.create_returns_tear_sheet(returns[trade_start:trade_end], 
 benchmark_rets=bm_rets[trade_start:trade_end])

Annualised volatility of the portfolio = 0.1174
Annualised volatility of the benchmark = 0.1305
Entire data start date: 2013年05月31日
Entire data end date: 2016年05月31日
Backtest Months: 36
 Backtest
annual_return 0.09
annual_volatility 0.12
sharpe_ratio 0.82
calmar_ratio 0.77
stability 0.85
max_drawdown -0.12
omega_ratio 1.15
sortino_ratio 1.19
skewness -0.04
kurtosis 2.57
information_ratio 0.01
alpha 0.03
beta 0.82
[see appendix below for full Pyfolio returns analytics]

Annualised volatility of the portfolio = 0.1174
Annualised volatility of the benchmark = 0.1305
Entire data start date: 2013年05月31日
Entire data end date: 2016年05月31日
Backtest Months: 36
 Backtest
annual_return 0.09
annual_volatility 0.12
sharpe_ratio 0.82
calmar_ratio 0.77
stability 0.85
max_drawdown -0.12
omega_ratio 1.15
sortino_ratio 1.19
skewness -0.04
kurtosis 2.57
information_ratio 0.01
alpha 0.03
beta 0.82
Worst Drawdown Periods
 net drawdown in % peak date valley date recovery date duration
0 12.23 2015年07月20日 2015年08月25日 2015年10月23日 70
1 6.46 2015年12月29日 2016年01月20日 2016年02月25日 43
4 6.42 2013年08月05日 2013年10月08日 2013年11月06日 68
2 6.39 2014年11月28日 2014年12月16日 2015年05月19日 123
3 6.39 2013年11月29日 2014年02月03日 2014年03月28日 86
2-sigma returns daily -0.014
2-sigma returns weekly -0.027
dtype: float64

C:\Anaconda3\envs\py34\lib\site-packages\pyfolio\plotting.py:1356: FutureWarning:
 sort(columns=....) is deprecated, use sort_values(by=.....)
 print(drawdown_df.sort('net drawdown in %', ascending=False))