def main():
pp = pprint.PrettyPrinter(indent=4)
# first get ticker price and volatility data
print ">> Getting ticker data..."
ticker_data = get_ticker_data()
ticker_volatilities = get_ticker_volatilities(ticker_data)
# then treat each group (like stocks) as its own portfolio and equalize volatility contributions
asset_class_weights = get_asset_class_weights(ticker_volatilities)
# then treat each box as its own portfolio and equalize volatility contributions
box_weights = get_box_weights(ticker_volatilities, asset_class_weights)
# then treat each box as an asset itself in a four-asset portfolio and equalize contributions
environment_weights = get_environment_weights(ticker_volatilities,
asset_class_weights,
box_weights)
# find individual asset weight by multiplying box_weights and environment_weights per my all weather configuration
weight_dict = finalize_ticker_weights(asset_class_weights,
environment_weights, box_weights)
print "\n>> Volatilities"
pp.pprint(ticker_volatilities)
print "\n>> Box weights"
pp.pprint(box_weights)
print "\n>> Environment weights"
pp.pprint(environment_weights)
print "\n>> Final weights"
pp.pprint(weight_dict)
update_weight_file(weight_dict)
backtesting.backtest(
weight_dict, output=True
) # yes, this is backtesting with weights we could have only known today, so it's not super rigorous
def main():
pp = pprint.PrettyPrinter(indent=4)
# first get ticker price and volatility data
print(">> Getting ticker data...")
ticker_data = get_ticker_data()
ticker_volatilities = get_ticker_volatilities(ticker_data)
# then treat each group (like stocks) as its own portfolio and equalize volatility contributions
asset_class_weights = get_asset_class_weights(ticker_volatilities)
# then treat each box as its own portfolio and equalize volatility contributions
box_weights = get_box_weights(ticker_volatilities, asset_class_weights)
# then treat each box as an asset itself in a four-asset portfolio and equalize contributions
environment_weights = get_environment_weights(ticker_volatilities, asset_class_weights, box_weights)
# find individual asset weight by multiplying box_weights and environment_weights per my all weather configuration
weight_dict = finalize_ticker_weights(asset_class_weights, environment_weights, box_weights)
print("\n>> Volatilities")
pp.pprint(ticker_volatilities)
print("\n>> Box weights")
pp.pprint(box_weights)
print("\n>> Environment weights")
pp.pprint(environment_weights)
print("\n>> Final weights")
pp.pprint(weight_dict)
update_weight_file(weight_dict)
backtesting.backtest(weight_dict, output=True) # yes, this is backtesting with weights we could have only known today, so it's not super rigorous
def main():
pp = pprint.PrettyPrinter(indent=4)
# first get ticker price and volatility data
print ">> Getting ticker data..."
ticker_data = get_ticker_data()
ticker_volatilities = get_ticker_volatilities(ticker_data)
# then treat each group (like stocks) as its own portfolio and equalize volatility contributions
asset_class_weights = get_asset_class_weights(ticker_volatilities)
# then treat each box as its own portfolio and equalize volatility contributions
box_weights = get_box_weights(ticker_volatilities, asset_class_weights)
# then treat each box as an asset itself in a four-asset portfolio and equalize contributions
environment_weights = get_environment_weights(ticker_volatilities,
asset_class_weights,
box_weights)
# find individual asset weight by multiplying box_weights and environment_weights per my all weather configuration
risk_weight_dict = finalize_ticker_weights(asset_class_weights,
environment_weights,
box_weights)
date = risk_weight_dict['Date']
del risk_weight_dict['Date']
# risk weight to value weight
ticker_data_pd = pd.DataFrame()
risk_weights = []
for key in ticker_data:
ticker_data_pd[key] = ticker_data[key]['Returns']
risk_weights.append(risk_weight_dict[key])
print ticker_data_pd
V = np.matrix(ticker_data_pd.dropna().corr())
weights = calcu_w(risk_weights, V, [0.1 for i in risk_weight_dict])
value_weight_dict = {}
for i, key in enumerate(ticker_data):
value_weight_dict[key] = weights[i]
print "\n>> Volatilities"
pp.pprint(ticker_volatilities)
print "\n>> Box weights"
pp.pprint(box_weights)
print "\n>> Environment weights"
pp.pprint(environment_weights)
print "\n>> Final risk weights"
pp.pprint(risk_weight_dict)
print "\n>> Final value weights"
pp.pprint(value_weight_dict)
update_weight_file(value_weight_dict)
result = backtesting.backtest(
value_weight_dict, output='all_weather_results'
) # yes, this is backtesting with weights we could have only known today, so it's not super rigorous
def merge_stocks():
"""
通过风险平价合成股票指数(ETF)
:return:
"""
pp = pprint.PrettyPrinter(indent=4)
# first get ticker price and volatility data
print ">> Getting ticker data..."
flag = 'etf' # etf or index
if flag == 'etf':
TICKERS = {
"us": ['SPY', 'QQQ', 'IWM'],
"developed": ['ZWY00', 'ZCY00', 'ZSY00'],
"emerging": ['GCY00', 'SIY00', 'HGY00'],
}
else:
TICKERS = {
"us": [],
"energies": [],
"grains": [],
}
weights_by_asset_predefined = {"us": 0.3, "developed": 0.3, "meats": 0.4}
ticker_data = get_ticker_data(TICKERS)
ticker_volatilities = get_ticker_volatilities(ticker_data)
# then treat each group (like stocks) as its own portfolio and equalize volatility contributions
asset_class_weights = get_asset_class_weights(TICKERS, ticker_volatilities)
# asset_class_weights = get_asset_class_weights_avg(TICKERS)
# find individual asset weight by multiplying box_weights and environment_weights per my all weather configuration
risk_weight_dict = finalize_ticker_weights(TICKERS, asset_class_weights,
weights_by_asset_predefined)
date = risk_weight_dict['Date']
del risk_weight_dict['Date']
# risk weight to value weight
ticker_data_pd = pd.DataFrame()
risk_weights = []
for key in ticker_data:
ticker_data_pd[key] = ticker_data[key]['Returns']
risk_weights.append(risk_weight_dict[key])
V = np.matrix(ticker_data_pd.dropna().corr())
weights = calcu_w(risk_weights, V, [0.1 for i in risk_weight_dict])
value_weight_dict = {}
for i, key in enumerate(ticker_data):
value_weight_dict[key] = weights[i]
print "\n>> Volatilities"
pp.pprint(ticker_volatilities)
print "\n>> Final risk weights"
pp.pprint(risk_weight_dict)
print "\n>> Final value weights"
pp.pprint(value_weight_dict)
result = backtesting.backtest(
value_weight_dict, output='commodities_results'
) # yes, this is backtesting with weights we could have only known today, so it's not super rigorous
result['Portfolio Returns'] = result['Portfolio Returns'].fillna(0)
comm_df = pd.DataFrame()
comm_df['close'] = (result['Portfolio Returns'].cumsum() + 1) * 100
comm_df['Returns'] = result['Portfolio Returns']
comm_df.to_csv('data/caihui/future/COMMODITIES_INDEX_MAIN_VOLUME')