股债比回归验证

gistfile1.py

import tushare as ts
import numpy as np
import pandas as pd
from datetime import datetime, date
from matplotlib import pyplot as plt

start_date = date(2015, 1, 1)
hs300 = 0
start_amount = 10000000
start_index = 0
debt_amount = start_amount
latest_index_trade_date = start_date
latest_debt_trade_date = start_date
latest_ratio = 0

def rebalance(d, index):
    global debt_amount, hs300, latest_index_trade_date
    total = debt_amount + hs300 * index # 计算下持仓总量
    target_hs300 = (total * latest_ratio) / index # 交易后的 HS300 持仓
    target_debt_amount = total * (1 - latest_ratio) # 交易后的债券持仓

    diff_hs300 = target_hs300 - hs300 # HS300 交易差
    diff_debt_amount = target_debt_amount - debt_amount # 债券交易差

    # 更新持仓
    hs300 = target_hs300
    debt_amount = target_debt_amount

    # TODO: 计算交易手续费

    # 更新交易数据
    latest_index_trade_date = d

def trade(d, ratio, index):
    global debt_amount, hs300, latest_index_trade_date, latest_ratio
    total = debt_amount + hs300 * index # 计算下持仓总量
    target_hs300 = (total * ratio) / index # 交易后的 HS300 持仓
    target_debt_amount = total * (1 - ratio) # 交易后的债券持仓

    diff_hs300 = target_hs300 - hs300 # HS300 交易差
    diff_debt_amount = target_debt_amount - debt_amount # 债券交易差

    # 更新持仓
    hs300 = target_hs300
    debt_amount = target_debt_amount

    # TODO: 计算交易手续费

    # 更新交易数据
    latest_index_trade_date = d
    latest_ratio = ratio

def calc_ratio(d, pe, debt):
    ratio = 1.0 / pe * 100.0 / debt
    ret = 0
    if ratio > 2.4:
        ret = 1.0
    elif ratio > 2.1:
        ret = 0.7
    elif ratio > 1.8:
        ret = 0.6
    elif ratio > 1.5:
        ret = 0.3

    # print(d, 'pe=', pe, '风险收益率=', 1.0/pe * 100.0, '无风险收益率=', debt, '股债比=', ratio)
    return ret

def get_debt_day_rate(debt):
    return (1.0 + debt / 100.0) ** (1/365) - 1.0

def increase_debt(d, debt):
    global latest_debt_trade_date
    days = (d - latest_debt_trade_date).days
    latest_debt_trade_date = d
    debt_amount * np.power(1.0 + get_debt_day_rate(debt), days)

def process(d, pe, index, debt):
    ratio = calc_ratio(d, pe, debt)

    debt_amount = increase_debt(d, debt)

    if (ratio == latest_ratio) and ((d - latest_index_trade_date).days > 180):
        rebalance(d, index)
    elif ratio != latest_ratio:
        trade(d, ratio, index)

df_index = pd.read_csv('index.csv', index_col=0)
df_debt = pd.read_csv('debt.csv')
df = df_index.merge(df_debt, left_on='trade_date', right_on='trade_date')

df = df.sort_values(by='trade_date')
latest_print_date = date(1970, 1, 1)
result = pd.DataFrame(columns = ['trade_date', 'index_close', 'pe_close', 'debt_close', '股债比', 'debt_holding', 'index_holding', 'ratio', 'yield', 'index_yield', 'amount'])
for index, row in df.iterrows():
    d = datetime.strptime(str(int(row['trade_date'])), '%Y%m%d').date()
    if d <= start_date:
        continue

    process(d, row['pe'], row['close_x'], row['close_y'])
    if start_index == 0:
        start_index = row['close_x']

    total = debt_amount + hs300 * row['close_x'] # 当前总资产
    result = result.append({
        'trade_date': d,
        'index_close': row['close_x'],
        'pe_close': row['pe'],
        'debt_close': row['close_y'] / 100,
        '股债比': 1/row['pe']/row['close_y'] * 100,
        'debt_holding': debt_amount,
        'index_holding': hs300 * row['close_x'],
        'ratio': latest_ratio,
        'yield': (total / start_amount) ** (1/((d - start_date).days / 365)) - 1,
        'index_yield': (row['close_x'] / start_index) ** (1/((d - start_date).days / 365)) - 1,
        'amount': total,
    }, ignore_index=True)

result.to_csv('result.csv')
print(result)