# -*- coding: utf-8 -*-

##

# @file finance.py

# @brief 常见的金融工具函数

# @author wondereamer

# @version 0.2

# @date 2015-12-09

import pandas as pd

import numpy as np

def sharpe_ratio(returns, periods=252):

"""

Create the Sharpe ratio for the strategy, based on a

benchmark of zero (i.e. no risk-free rate information).

Args:

returns (list, Series) - A pandas Series representing

period percentage returns.

periods (int.) Daily (252), Hourly (252*6.5), Minutely(252*6.5*60) etc.

Returns:

float. The result

"""

return np.sqrt(periods) * (np.mean(returns)) / np.std(returns)

def max_drawdown(networth):

""" 统计最大回测和相应的回测周期。

networth: 历史净值

"""

hwm = [0] # 历史最大值序列变量

eq_idx = networth.index

drawdown = pd.Series(index=eq_idx)

duration = pd.Series(index=eq_idx)

for t in range(1, len(eq_idx)):

cur_hwm = max(hwm[t-1], networth[t])

hwm.append(cur_hwm)

drawdown[t] = hwm[t] - networth[t]

# <=0 新高，计数0

duration[t] = 0 if drawdown[t] <= 0 else duration[t-1] + 1

return drawdown.max(), duration.max()

def create_equity_curve(all_holdings):

""" 创建资金曲线, 历史回报率对象。

Args:

all_holdings (list): 账号资金历史。

Returns:

pd.DataFrame 数据列 { 'cash', 'commission',

'equity', 'returns', 'networth' }

"""

curve = pd.DataFrame(all_holdings)

curve.set_index('datetime', inplace=True)

curve['returns'] = curve['equity'].pct_change()

curve.iloc[0, 3] = 0 # reset first value as 0 instead nan

curve['networth'] = (1.0+curve['returns']).cumprod()

return curve

def summary_stats(curve, periods):

"""

策略统计。

periods - Daily (252), Hourly (252*6.5), Minutely(252*6.5*60) etc.

"""

total_return = curve['networth'][-1]

returns = curve['returns']

pnl = curve['networth']

sharpe = sharpe_ratio(returns, periods)

max_dd, dd_duration = max_drawdown(pnl)

stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),

("Sharpe Ratio", "%0.2f" % sharpe),

("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),

("Drawdown Duration", "%d" % dd_duration)]

return stats