def rebalancing_porfolio(symbols=None, bench='^VNINDEX'):
start0 = "2015-1-2"
end0 = "2017-1-2"
allocations, cr, adr, sddr, sr = optimize_portfolio(sd=start0,
ed=end0,
syms=symbols,
benchmark=bench,
gen_plot=True)
print("Optimize start Date:", start0)
print("Optimize end Date:", end0)
print("Optimize volatility (stdev of daily returns):", sddr)
print("Optimize average Daily Return:", adr)
print("Optimize cumulative Return:", cr)
print(" -----------------------------------------------------")
start_date_list = ["2017-1-3", "2017-7-3"]
end_date_list = ["2017-7-2", "2018-4-1"]
for start, end in zip(start_date_list, end_date_list):
cr, adr, sddr, sr = compute_portfolio(sd=start,
ed=end,
syms=symbols,
allocs=allocations,
benchmark=bench,
gen_plot=True)
print("Start Date:", start)
print("End Date:", end)
print("Volatility (stdev of daily returns):", sddr)
print("Average Daily Return:", adr)
print("Cumulative Return:", cr)
print(" -----------------------------------------------------")
allocations, cr, adr, sddr, sr = optimize_portfolio(sd=start,
ed=end,
syms=symbols,
benchmark=bench,
gen_plot=False)
print("Optimize volatility (stdev of daily returns):", sddr)
print("Optimize average Daily Return:", adr)
print("Optimize cumulative Return:", cr)
print(" -----------------------------------------------------")
# Out of sample testing optimisation algorithm
end_date = "2018-9-27"
start_date = "2018-4-2"
cr, adr, sddr, sr = compute_portfolio(sd=start_date,
ed=end_date,
syms=symbols,
allocs=allocations,
benchmark=bench,
gen_plot=True)
print(
"....................... Out of sample performance .................")
print("Start Date:", start_date)
print("End Date:", end_date)
print("Volatility (stdev of daily returns):", sddr)
print("Average Daily Return:", adr)
print("Cumulative Return:", cr)
# Assess the portfolio
investment = 60E6
df_result = pd.DataFrame(index=symbols)
df_result['Opt allocs'] = allocations
df_result['Cash'] = allocations * investment
dates = pd.date_range(start_date, end_date) # date range as index
df_data = get_data(symbols, dates,
benchmark=bench) # get data for each symbol
df_high = get_data(symbols, dates, benchmark=bench, colname='<High>')
df_low = get_data(symbols, dates, benchmark=bench, colname='<Low>')
max_high = pd.Series(df_high.max(), name='MaxHigh')
min_low = pd.Series(df_low.min(), name='MinLow')
cpm = pd.Series(max_high / min_low, name='CPM')
volatility = df_data[symbols].pct_change().std()
# Fill missing values
df_result['Close'] = df_data[symbols].iloc[-1, :].values
df_result['CPM'] = cpm[symbols]
df_result['Shares'] = round(
df_result['Cash'] / df_result['Close'].values / 1000, 0)
df_result['Volatility'] = volatility
alpha_beta = analysis_alpha_beta(df_data, symbols, market=bench)
df_result['Alpha'] = alpha_beta['Alpha']
df_result['Beta'] = alpha_beta['Beta']
relative_strength = 40*df_data[symbols].pct_change(periods = 63).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 126).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 189).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 252).fillna(0)
df_result['RSW'] = relative_strength.iloc[-1, :].values
return df_result
def passive_strategy(start_date, end_date, market="SPY"):
symbols = getliststocks(typestock=market)
dates = pd.date_range(start_date, end_date) # date range as index
df_data = get_data_us(symbols, dates,
benchmark=market) # get data for each symbol
df_volume = get_data_us(symbols, dates, benchmark=market,
colname='Volume') # get data for each symbol
df_high = get_data_us(symbols, dates, benchmark=market, colname='High')
df_low = get_data_us(symbols, dates, benchmark=market, colname='Low')
df_value = (df_volume * df_data).fillna(0)
valueM30 = df_value.rolling(window=30).mean()
vol_mean = pd.Series(df_volume.mean(), name='Volume')
max_high = pd.Series(df_high.max(), name='MaxHigh')
min_low = pd.Series(df_low.min(), name='MinLow')
cpm = pd.Series(max_high / min_low, name='CPM')
value_mean = pd.Series(df_value.mean(), name='Value')
# Fill missing values
fill_missing_values(df_data)
# Assess the portfolio
allocations, cr, adr, sddr, sr = optimize_portfolio(sd=start_date,
ed=end_date,
syms=symbols,
benchmark=market,
country='US',
gen_plot=True)
# Print statistics
print("Start Date:", start_date)
print("End Date:", end_date)
print("Symbols:", symbols)
print("Optimal allocations:", allocations)
print("Sharpe Ratio:", sr)
print("Volatility (stdev of daily returns):", sddr)
print("Average Daily Return:", adr)
print("Cumulative Return:", cr)
investment = 50000000
df_result = pd.DataFrame(index=symbols)
df_result['Opt allocs'] = allocations
df_result['Cash'] = allocations * investment
df_result['Close'] = df_data[symbols].iloc[-1, :].values
df_result['Volume'] = df_volume[symbols].iloc[-1, :].values
df_result['VolumeMean'] = vol_mean[symbols]
df_result['Value'] = df_result['Close'] * df_result['Volume']
df_result['ValueMean'] = value_mean[symbols]
df_result['ValueMA30'] = valueM30[symbols].iloc[-1, :].values
# df_result['MaxH'] = max_high
# df_result['MinL'] = min_low
df_result['CPM'] = cpm[symbols]
df_result['Shares'] = round(
df_result['Cash'] / df_result['Close'].values / 1000, 0)
df_result['Volatility'] = df_data[symbols].pct_change().std()
alpha_beta = analysis_alpha_beta(df_data, symbols, market)
df_result['Alpha'] = alpha_beta['Alpha']
df_result['Beta'] = alpha_beta['Beta']
relative_strength = 40*df_data[symbols].pct_change(periods = 63).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 126).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 189).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 252).fillna(0)
df_result['RSW'] = relative_strength.iloc[-1, :].values
return df_result, df_data
def passive_strategy(start_date, end_date, market="^VNINDEX", symbols=None):
if symbols == None:
symbols = getliststocks(typestock=market)
dates = pd.date_range(start_date, end_date) # date range as index
df_data = get_data(symbols, dates,
benchmark=market) # get data for each symbol
df_volume = get_data(symbols, dates, benchmark=market,
colname='<Volume>') # get data for each symbol
df_high = get_data(symbols, dates, benchmark=market, colname='<High>')
df_low = get_data(symbols, dates, benchmark=market, colname='<Low>')
# covariance = numpy.cov(asset , SPY)[0][1]
# variance = numpy.var(asset)
#
# beta = covariance / variance
df_volume = df_volume.fillna(0)
df_value = (df_volume * df_data).fillna(0)
valueM30 = df_value.rolling(window=30).mean()
vol_mean = pd.Series(df_volume.mean(), name='Volume')
max_high = pd.Series(df_high.max(), name='MaxHigh')
min_low = pd.Series(df_low.min(), name='MinLow')
cpm = pd.Series(max_high / min_low, name='CPM')
value_mean = pd.Series(df_value.mean(), name='ValueMean')
# Fill missing values
fill_missing_values(df_data)
# Assess the portfolio
allocations, cr, adr, sddr, sr = optimize_portfolio(sd=start_date,
ed=end_date,
syms=symbols,
benchmark=market,
gen_plot=True)
# Print statistics
print("Start Date:", start_date)
print("End Date:", end_date)
print("Symbols:", symbols)
print("Optimal allocations:", allocations)
print("Sharpe Ratio:", sr)
print("Volatility (stdev of daily returns):", sddr)
print("Average Daily Return:", adr)
print("Cumulative Return:", cr)
investment = 50000000
df_result = pd.DataFrame(index=symbols)
df_result['Opt allocs'] = allocations
df_result['Cash'] = allocations * investment
df_result['Close'] = df_data[symbols].iloc[-1, :].values
df_result['PCT_Change'] = 100 * (df_data[symbols].iloc[-1, :].values -
df_data[symbols].iloc[0, :].values
) / df_data[symbols].iloc[0, :].values
df_result['Volume'] = df_volume[symbols].iloc[-1, :].values
df_result['VolumeMean'] = vol_mean[symbols]
df_result['Value'] = df_result['Close'] * df_result['Volume']
df_result['ValueMean'] = value_mean[symbols]
df_result['ValueMA30'] = valueM30[symbols].iloc[-1, :].values
# df_result['MaxH'] = max_high
# df_result['MinL'] = min_low
df_result['CPM'] = cpm[symbols]
df_result['Shares'] = round(
df_result['Cash'] / df_result['Close'].values / 1000, 0)
df_result['Volatility'] = df_data[symbols].pct_change().std()
df_result['PCT_Change0D'] = df_data[symbols].pct_change().iloc[
-1, :].values * 100
df_result['PCT_Change1D'] = df_data[symbols].pct_change().iloc[
-2, :].values * 100
df_result['PCT_Change2D'] = df_data[symbols].pct_change().iloc[
-3, :].values * 100
alpha_beta = analysis_alpha_beta(df_data, symbols, market)
df_result['Alpha'] = alpha_beta['Alpha']
df_result['Beta'] = alpha_beta['Beta']
relative_strength = 40*df_data[symbols].pct_change(periods = 63).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 126).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 189).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 252).fillna(0)
relative_strength1M = 100 * df_data[symbols].pct_change(
periods=21).fillna(0)
relative_strength2M = 100 * df_data[symbols].pct_change(
periods=42).fillna(0)
df_result['RSW'] = relative_strength.iloc[-1, :].values
df_result['RSW1M'] = relative_strength1M.iloc[-1, :].values
df_result['RSW2M'] = relative_strength2M.iloc[-1, :].values
marketVNI = df_data[symbols].pct_change()
advances = marketVNI[marketVNI > 0]
declines = marketVNI[marketVNI <= 0]
dec = advances.isnull().sum(axis=1)
adv = declines.isnull().sum(axis=1)
df_market = pd.DataFrame(index=marketVNI.index)
df_market[market + 'Volume'] = df_volume[market]
df_market[market + 'PCT_Volume'] = df_volume[market].pct_change() * 100
df_market[market + 'PCT_Index'] = df_data[market].pct_change() * 100
# df_market['Adv'] = adv
# df_market['Dec'] = dec
df_market[market + 'Adv_Dec'] = adv - dec
df_market[market + 'Dec/Adv'] = dec / adv
strength = pd.Series(index=marketVNI.index)
strength[(df_market[market + 'Adv_Dec'] > 0)
& (df_market[market + 'PCT_Index'] > 0)] = 1
strength[(df_market[market + 'Adv_Dec'] < 0)
& (df_market[market + 'PCT_Index'] < 0)] = -1
strength[(df_market[market + 'Adv_Dec'] < 0)
& (df_market[market + 'PCT_Index'] > 0)] = 0
df_market[market + 'Strength'] = strength
# np.where((df_data[symbols].pct_change() > 0), 1, -1)
return df_result, df_data, df_market