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June 24, 2019 12:53
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import pandas_datareader.data as pdr | |
import matplotlib.pyplot as plt | |
import pandas as pd | |
from datetime import datetime, timezone | |
import pytz | |
import talib | |
import itable | |
import ffn | |
from fintools import get_DataArray,compute_weights_RS_DM,compute_weights_PMA,\ | |
Parameters, show_return_table, show_annual_returns, \ | |
endpoints, backtest | |
start = datetime(2000, 1, 1, 0, 0, 0, 0, pytz.utc) | |
end = datetime.today().replace(tzinfo=timezone.utc) | |
# CHOOSE ONLY ONE | |
strategies = { | |
'PMA001': {'assets': ['VCVSX', 'VFIIX'], | |
'start':start, 'end':end, | |
'risk_lookback': 3, 'frequency': 'M', 'allocations': [0.6, 0.4], | |
'cash_proxy': 'VUSTX'}, | |
# 'PMA002': {'assets': ['VCVSX', 'VWINX', 'VWEHX'], | |
# 'start':start, 'end':end, | |
# 'risk_lookback': 3, 'frequency': 'M', 'allocations': [0.6, 0.2, 0.2], | |
# 'cash_proxy': 'VUSTX'}, | |
# 'PMA003': {'assets': ['VCVSX', 'FAGIX', 'VGHCX'], | |
# 'start':start, 'end':end, | |
# 'risk_lookback': 2, 'frequency': 'M', 'allocations': [1./3., 1./3., 1./3.], | |
# 'cash_proxy': 'VUSTX'} | |
} | |
# ***************************************************************** | |
# Load historical data | |
# ****************************************************************** | |
name = [i for i in strategies.items()][0][0] | |
p = Parameters(strategies[name]) | |
cash_proxy = p.cash_proxy | |
rs_lookback = None | |
risk_lookback = p.risk_lookback | |
risk_free = None | |
allocations = p.allocations | |
assets = p.assets | |
# get data | |
tickers = assets.copy() | |
if cash_proxy != 'CASHX': | |
tickers = list(set(tickers + [cash_proxy])) | |
if isinstance(risk_free, str): | |
tickers = list(set(tickers + [risk_free])) | |
da = get_DataArray(tickers, start, end) | |
# don't forget to ffill() | |
data = da.to_pandas().transpose(1, 2, 0).ffill() | |
data1 = data.copy()[:, :, 'adj close'] | |
inception_dates = pd.DataFrame([data1[ticker].first_valid_index() for ticker in data1.columns], | |
index=data1.keys(), columns=['inception']) | |
# print (inception_dates) | |
prices = data.copy()[:, :, 'adj close'].dropna() | |
end_points = endpoints(period=p.frequency, trading_days=prices.index) | |
prices_m = prices.loc[end_points] | |
# print(prices_m[:3]) | |
# elligibility rule | |
SMA = prices_m.rolling(p.risk_lookback).mean().dropna() | |
rebalance_dates = SMA.index | |
rule = prices_m.loc[rebalance_dates][p.assets] > SMA[p.assets] | |
# fixed weight allocation | |
weights = p.allocations * rule | |
# downside protection | |
weights[p.cash_proxy] = 1 - weights[p.assets].sum(axis=1) | |
# backtest | |
p_value, p_holdings, p_weights = backtest(prices, weights, 10000., offset=0, commission=10.) | |
p_value.plot(figsize=(15, 10), grid=True, legend=True, label=name) | |
# p_value.plot(figsize=(15, 10), grid=True, legend=True, label=name) | |
ffn.calc_perf_stats(p_value).display() | |
# show_return_table(p_value) | |
# | |
# show_annual_returns(p_value) |
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import matplotlib.pyplot as plt | |
import pandas as pd | |
from datetime import datetime, timezone | |
import pytz | |
import ffn | |
from fintools import get_DataArray,compute_weights_RS_DM,compute_weights_PMA,\ | |
Parameters, show_return_table, show_annual_returns, \ | |
endpoints, backtest | |
start = datetime(2000, 1, 1, 0, 0, 0, 0, pytz.utc) | |
end = datetime.today().replace(tzinfo=timezone.utc) | |
# CHOOSE ONLY ONE | |
strategies = { | |
# 'RS0001': { 'assets': ['VCVSX','VWEHX','VFIIX','FGOVX','VWAHX'], | |
# 'start':start, 'end':end, | |
# 'rs_lookback': 1, 'risk_lookback': 1, 'n_top': 2, 'frequency': 'M', | |
# 'cash_proxy': 'CASHX', 'risk_free': 0}, | |
# 'RS0002': {'assets': ['MMHYX','FAGIX','VFIIX'], | |
# 'start':start, 'end':end, | |
# 'rs_lookback': 3, 'risk_lookback': 2, 'n_top': 1, 'frequency': 'M', | |
# 'cash_proxy': 'CASHX', 'risk_free': 0}, | |
# 'RS0003': {'assets': ['MMHYX','FAGIX','VFIIX'], | |
# 'start':start, 'end':end, | |
# 'rs_lookback': 1, 'risk_lookback': 1, 'n_top': 1, 'frequency': 'Q', | |
# 'cash_proxy': 'CASHX', 'risk_free': 0}, | |
'DM0001': {'assets': ['VCVSX', 'VWINX', 'VWEHX', 'VGHCX', 'VFIIX', 'VWAHX', 'FGOVX', 'FFXSX'], | |
'start': start, 'end': end, | |
'rs_lookback': 1, 'risk_lookback': 1, 'n_top': 3, 'frequency': 'M', | |
'cash_proxy': 'VUSTX', 'risk_free': 0}, | |
# 'DM0002': {'assets': ['VCVSX','VUSTX','VWEHX','VFIIX','VGHCX','FRESX'], | |
# 'start':start, 'end':end, | |
# 'rs_lookback': 1, 'risk_lookback': 1, 'n_top': 5, 'frequency': 'M', | |
# 'cash_proxy': 'VFIIX', 'risk_free': 'FFXSX'}, | |
} | |
# ***************************************************************** | |
# Load historical data | |
# ****************************************************************** | |
name = [i for i in strategies.items()][0][0] | |
p = Parameters(strategies[name]) | |
cash_proxy = p.cash_proxy | |
risk_free = p.risk_free | |
rs_lookback = p.rs_lookback | |
risk_lookback = p.risk_lookback | |
n_top = p.n_top | |
# allocations = p.allocations | |
assets = p.assets | |
# get data | |
tickers = assets.copy() | |
if cash_proxy != 'CASHX': | |
tickers = list(set(tickers + [cash_proxy])) | |
if isinstance(risk_free, str): | |
tickers = list(set(tickers + [risk_free])) | |
da = get_DataArray(tickers, start, end) | |
# don't forget to ffill() | |
data = da.to_pandas().transpose(1, 2, 0).ffill() | |
data1 = data.copy()[:, :, 'adj close'] | |
inception_dates = pd.DataFrame([data1[ticker].first_valid_index() for ticker in data1.columns], | |
index=data1.keys(), columns=['inception']) | |
# print (inception_dates) | |
prices = data.copy()[:, :, 'adj close'].dropna() | |
end_points = endpoints(period=p.frequency, trading_days=prices.index) | |
prices_m = prices.loc[end_points] | |
# print(prices_m[:3]) | |
returns = prices_m[p.assets].pct_change(p.rs_lookback)[p.rs_lookback:] | |
if isinstance(p.risk_free, int): | |
excess_returns = returns | |
else: | |
risk_free_returns = prices_m[p.risk_free].pct_change(p.rs_lookback)[p.rs_lookback:] | |
excess_returns = returns.subtract(risk_free_returns, axis=0).dropna() | |
absolute_momentum = prices_m[p.assets].pct_change(p.risk_lookback)[p.risk_lookback:] | |
absolute_momentum_rule = absolute_momentum > 0 | |
rebalance_dates = excess_returns.index.join(absolute_momentum_rule.index, how='inner') | |
# relative strength ranking | |
ranked = excess_returns.loc[rebalance_dates][p.assets].rank(ascending=False, axis=1, method='dense') | |
# elligibility rule - top n_top ranked securities | |
elligible = ranked[ranked <= p.n_top] > 0 | |
# equal weight allocations | |
elligible = elligible.multiply(1. / elligible.sum(1), axis=0) | |
# downside protection | |
weights = pd.DataFrame(0., index=elligible.index, columns=prices.columns) | |
if p.cash_proxy == 'CASHX': | |
weights[p.cash_proxy] = 0 | |
prices[p.cash_proxy] = 1. | |
weights[p.assets] = (elligible * absolute_momentum_rule).dropna() | |
weights[p.cash_proxy] += 1 - weights[p.assets].sum(axis=1) | |
# backtest | |
p_value, p_holdings, p_weights = backtest(prices, weights, 10000., offset=0, commission=10.) | |
# p_value.plot(figsize=(15, 10), grid=True, legend=True, label=name) | |
ffn.calc_perf_stats(p_value).display() | |
# show_return_table(p_value) | |
# | |
# show_annual_returns(p_value) |
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