import numpy as np
import pandas as pd
import yfinance as yf
import warnings
import riskfolio as rp

warnings.filterwarnings("ignore")
pd.options.display.float_format = '{:.2%}'.format

# Date range
start = '2000-01-01'
end = '2022-07-31'

# Tickers of assets
assets = """AXISBANK.NS	BPCL.NS	BRITANNIA.NS	CIPLA.NS	DRREDDY.NS	EICHERMOT.NS	GAIL.NS	HDFCBANK.NS	HINDALCO.NS	HINDUNILVR.NS	INFY.NS	IOC.NS	ITC.NS	M&M.NS	MARICO.NS	ONGC.NS	RELIANCE.NS	SAIL.NS	SBIN.NS	SIEMENS.NS	SUNPHARMA.NS	TATACONSUM.NS	TATAMOTORS.NS	TATAPOWER.NS	TATASTEEL.NS	TITAN.NS	VEDL.NS	WIPRO.NS""".split("\t")

#assets = [x + ".NS" for x in assets]
assets.sort()

# Downloading data
data = yf.download(assets, start=start, end=end)
data = data.loc[:, ('Adj Close', slice(None))]
data.columns = assets
Y = data[assets].pct_change().dropna()
display(Y.head())

[*********************100%***********************]  28 of 28 completed

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	AXISBANK.NS	BPCL.NS	BRITANNIA.NS	CIPLA.NS	DRREDDY.NS	EICHERMOT.NS	GAIL.NS	HDFCBANK.NS	HINDALCO.NS	HINDUNILVR.NS	...	SBIN.NS	SIEMENS.NS	SUNPHARMA.NS	TATACONSUM.NS	TATAMOTORS.NS	TATAPOWER.NS	TATASTEEL.NS	TITAN.NS	VEDL.NS	WIPRO.NS
Date
2000-01-04	3.86%	-7.20%	-0.31%	0.54%	8.00%	5.22%	-3.42%	2.24%	3.79%	-1.15%	...	6.34%	0.29%	-1.34%	-2.41%	-3.94%	0.20%	-1.08%	-5.33%	-1.42%	8.00%
2000-01-05	-2.60%	-2.85%	-2.55%	-2.06%	-3.74%	8.07%	-4.75%	-3.94%	8.00%	-3.56%	...	-4.11%	-4.40%	-3.51%	-4.17%	2.43%	1.95%	3.81%	-6.11%	-0.61%	1.63%
2000-01-06	-2.48%	5.65%	6.85%	-5.52%	5.96%	8.01%	2.77%	0.81%	7.83%	4.01%	...	5.05%	1.44%	-0.10%	3.34%	4.15%	3.12%	7.47%	8.02%	4.65%	-1.93%
2000-01-07	-4.89%	-0.12%	8.00%	-7.99%	-7.97%	7.75%	-3.54%	0.03%	4.44%	5.45%	...	4.71%	-7.96%	-8.00%	7.97%	8.01%	-0.19%	2.20%	-2.11%	-2.35%	-8.00%
2000-01-10	3.09%	1.34%	7.51%	-3.34%	-1.65%	1.31%	-0.56%	-1.46%	-6.58%	0.76%	...	-1.39%	-1.82%	-4.59%	0.88%	7.90%	7.00%	5.99%	-0.99%	2.15%	0.14%

5 rows × 28 columns

prices = data
display(prices.dropna().head())

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	AXISBANK.NS	BPCL.NS	BRITANNIA.NS	CIPLA.NS	DRREDDY.NS	EICHERMOT.NS	GAIL.NS	HDFCBANK.NS	HINDALCO.NS	HINDUNILVR.NS	...	SBIN.NS	SIEMENS.NS	SUNPHARMA.NS	TATACONSUM.NS	TATAMOTORS.NS	TATAPOWER.NS	TATASTEEL.NS	TITAN.NS	VEDL.NS	WIPRO.NS
Date
2000-01-03	369.93%	1301.46%	5437.09%	10032.76%	32412.28%	289.18%	421.34%	1429.23%	5724.06%	13538.52%	...	1642.36%	4404.91%	1212.04%	3477.51%	3293.15%	430.95%	204.14%	698.82%	100.56%	8354.18%
2000-01-04	384.22%	1207.74%	5420.21%	10087.14%	35006.13%	304.27%	406.92%	1461.18%	5941.15%	13383.04%	...	1746.50%	4417.84%	1195.79%	3393.73%	3163.25%	431.79%	201.93%	661.56%	99.13%	9022.56%
2000-01-05	374.22%	1173.35%	5281.93%	9879.24%	33697.38%	328.84%	387.61%	1403.59%	6416.26%	12906.93%	...	1674.71%	4223.44%	1153.81%	3252.35%	3239.97%	440.21%	209.63%	621.17%	98.53%	9169.60%
2000-01-06	364.94%	1239.68%	5643.61%	9334.00%	35706.70%	355.18%	398.34%	1414.94%	6918.42%	13425.00%	...	1759.31%	4284.07%	1152.69%	3361.08%	3374.43%	453.97%	225.29%	670.99%	103.11%	8992.20%
2000-01-07	347.08%	1238.22%	6095.08%	8588.44%	32860.35%	382.71%	384.23%	1415.36%	7225.26%	14156.90%	...	1842.22%	3942.96%	1060.49%	3629.05%	3644.87%	453.13%	230.25%	656.85%	100.69%	8272.91%

5 rows × 28 columns

# Building the portfolio object
port = rp.Portfolio(returns=Y)

# Calculating optimal portfolio

# Select method and estimate input parameters:

# Method to estimate expected returns based on historical data.
method_mu = 'hist'
# Method to estimate covariance matrix based on historical data.
method_cov = 'hist'

port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)
# Estimate optimal portfolio:
port.mu = pd.DataFrame(
     np.sqrt(np.diag(port.cov)).reshape(-1, 28), columns=assets)
# Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)
model = 'Classic'
rm = 'MV'  # Risk measure used, this time will be variance
obj = 'Sharpe'  # Objective function, could be MinRisk, MaxRet, Utility or Sharpe
hist = True  # Use historical scenarios for risk measures that depend on scenarios
rf = 0 # Risk free rate
l = 0  # Risk aversion factor, only useful when obj is 'Utility'

w1 = port.optimization(model=model, rm=rm,
                       obj=obj, rf=rf, l=l, hist=hist)
# Estimate points in the efficient frontier mean - semi standard deviation

# Estimate the risk parity portfolio for semi standard deviation
display(w1.T)

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	AXISBANK.NS	BPCL.NS	BRITANNIA.NS	CIPLA.NS	DRREDDY.NS	EICHERMOT.NS	GAIL.NS	HDFCBANK.NS	HINDALCO.NS	HINDUNILVR.NS	...	SBIN.NS	SIEMENS.NS	SUNPHARMA.NS	TATACONSUM.NS	TATAMOTORS.NS	TATAPOWER.NS	TATASTEEL.NS	TITAN.NS	VEDL.NS	WIPRO.NS
weights	2.85%	0.00%	2.30%	2.16%	10.66%	4.21%	0.00%	4.06%	0.83%	9.67%	...	0.00%	2.46%	10.76%	1.66%	1.48%	1.12%	4.49%	5.53%	2.91%	1.63%

1 rows × 28 columns

cagr_graph = rp.plot_series(returns=Y, w=w1, cmap='tab20', height=6, width=10,
                    ax=None)

ax = rp.plot_pie(w=w1, title='Max Diversification Asset Allocation', others=0.05, nrow=25, cmap="tab20",
                 height=6, width=10, ax=None)

ax = rp.plot_risk_con(w=w1, cov=cov, returns=Y, rm=rm,
                      rf=0, alpha=0.05, color="tab:blue", height=6,
                      width=10, t_factor=252, ax=None, )

ax = rp.plot_table(returns=Y, w=w1, MAR=0, alpha=0.05, ax=None)

ax = rp.jupyter_report(returns=Y,
                       w=w1,
                       rm=rm,
                       rf=0,
                       alpha=0.05,
                       others=0.05,
                       nrow=25,
                       height=6,
                       width=14,
                       t_factor=252,
                       ini_days=1,
                       days_per_year=252,
                       bins=50,
                       )

rp.excel_report(returns=Y,
                w=w1,
                rf=0,
                alpha=0.05,
                t_factor=252,
                ini_days=1,
                days_per_year=252,
                name="report")