sc-backtest 0.0.9

Index future simple stat and time-series backtest module

Index Futures Simple Backtest Module (Personal Usage)

Chang Sun Email

Install and Update

pip install --upgrade sc-backtest

Simple Test

• Check for factor validity
  • Statistical:
    • CDF
    • Markout
    • Hist
  • Time-Series:
    • Sign-Trade
    • Value-Trade
    • Threshold-Trade
  • Backtest
    • get_report
    • get_pnl_plot

# x: factors
# y: asset's future ret

import pandas as pd
import numpy as np
from sc_backtest import simpletest

data = pd.read_csv('.\factor_and_ret.csv', index_col=0, header=0)
x = data.loc[:, 'factor']
y = data.loc[:, 'ret']
st = simpletest(is_day=True)
st.plot_cdf(x, y)

# x: factors
# y: asset's future ret

import pandas as pd
import numpy as np
from sc_backtest import simpletest, bt

data = pd.read_csv('.\factor_and_ret.csv', index_col=0, header=0)
x = data.loc[:, 'factor']
y = data.loc[:, 'ret']
st = sb.simpletest(is_day=True)
data = st.simple_pnl(x, y, data_return=True)
report = bt.get_report(data['delta_med'], y)
bt.get_pnl_plot(data['delta_med'], y)

Technical Analysis (ta)

Reference: ta

Technical Analysis2 (ta2)

Variou moving average function

• SMA
• EMA
• WMA
• MMA
• QMA
• ...

import pandas as pd
import numpy as np
from sc_backtest import ta2

wma = ta2.wma(pd.Series(np.random.rand(100)), window=5)

Example

Input your factor and underlying asset's future return series with index type as DatetimeIndex and get the composite stat and time-series plots.

# x: factors
# y: asset's future ret

import pandas as pd
import numpy as np
from sc_backtest import simpletest

data = pd.read_csv('.\factor_and_ret.csv', index_col=0, header=0)
x = data.loc[:, 'factor']
y = data.loc[:, 'ret']
st = simpletest(is_day=True)
st.plot_composite(x, y, markout_periods=30, cdf_period2=5)