Python 投资组合风险管理入门
Dakota Wixom
Quantitative Analyst | QuantCourse.com
$$ R_p = R_{a_1} w_{a_1} + R_{a_2} w_{a_2} + ... + R_{a_n} w_{a_1} $$
假设 StockReturns 是股票收益的 pandas DataFrame,可按下述权重计算组合收益:
import numpy as np
portfolio_weights = np.array([0.25, 0.35, 0.10, 0.20, 0.10])
port_ret = StockReturns.mul(portfolio_weights, axis=1).sum(axis=1)
port_ret
Date
2017-01-03 0.008082
2017-01-04 0.000161
2017-01-05 0.003448
...
StockReturns["Portfolio"] = port_ret
假设 StockReturns 是股票收益的 pandas DataFrame,可按下述方法计算等权重组合收益:
import numpy as np
numstocks = 5
portfolio_weights_ew = np.repeat(1/numstocks, numstocks)
StockReturns.iloc[:,0:numstocks].mul(portfolio_weights_ew, axis=1).sum(axis=1)
Date
2017-01-03 0.008082
2017-01-04 0.000161
2017-01-05 0.003448
...
在 Python 中绘制日度收益:
StockPrices["Returns"] = StockPrices["Adj Close"].pct_change()
StockReturns = StockPrices["Returns"]
StockReturns.plot()

要绘制多个组合的累计收益:
import matplotlib.pyplot as plt
CumulativeReturns = ((1 + StockReturns).cumprod() - 1)
CumulativeReturns[["Portfolio","Portfolio_EW"]].plot()


市值(Market capitalization):公司公开交易股票的总价值。
亦称为 市值(Market cap)。
计算某只股票 n 的市值加权权重:
$$ w_{mcap_n} = \frac{mcap_n}{ \sum_{i=1}^n mcap_i } $$
在 Python 中计算市值权重,假设已获取各公司的市值数据:
import numpy as np
market_capitalizations = np.array([100, 200, 100, 100])
mcap_weights = market_capitalizations/sum(market_capitalizations)
mcap_weights
array([0.2, 0.4, 0.2, 0.2])
Python 投资组合风险管理入门