A small example package for CRI PMT test.
Project description
hdk_pkg_critest
This is a demo package for CRI test. This package is for processing kline data and running strategy backtest.
Installation
Use the package manager pip to install hdk_pkg_critest.
pip install hdk_pkg_critest
Quick Start
The package can be run on shell or CMD, but better to use an IDE (Try Spyder so that you can check the outputs clearly.
This chapter gives a simple demo of doing a momentum strategy backtest using the package. All the codes can also be found in demo.py
.
Please refer to next chapter for detailed documentation on the package.
STEP 1: Import the Package and Initialization
First, let's import the package, then initialize four main classes inside.
Once you initialize an instance of a class, you can easily call methods (functions) from it by doing this: return = instance.function(Args..)
import hdk_pkg_critest
sampledata = critest.SampleData() # a class packages the methods for fetching the sample data set
process = critest.Process() # a class packages the methods for processing data
backtest = critest.BackTest() # a class packages the methods for running backtesting
plotting = critest.Plotting() # a class packages the methods for Plotting the backtest results
STEP 2: Get a Sample Data Set for Test
Let's use the get_sampledata()
from the sampledata
to get a sample data set. Use data.head()
to check the data.
data = sampledata.get_sampledata() # a time-series data set of a future's kline
data.head() # check what the data set looks like
STEP 3: Process the Data to Generate Factors
There are some kline process funcions in the class process
. You can call and apply any of them on the data. For a momentum strategy, we need to process the kline as below:
#1 transfer the 1min kline to 60mins kline:
data_60 = process.chg_Granularity(data_1min=data, granularity=60)
data_60.head()
#2 generate HA kline:
data_HA = process.get_BarHA(Open=data_60.open, High=data_60.high, Low=data_60.low, Close=data_60.close)
data_HA.head()
#3 generate PS factor:
data_PS = process.get_BarPS(HA_Open=data_HA.HA_Open, HA_Close=data_HA.HA_Close, HA_PS_Lookback=20, PS_pct_level=[0.35, 0.5, 0.95, 0.97], combine=False)
data_PS.head()
#4 generate CT factor:
data_CT = process.get_BarCT(HA_Open=data_HA.HA_Open, HA_Close=data_HA.HA_Close, HA_PS=data_PS, bar_pass_cut=2, ps_pass_cut=2)
data_CT.head()
STEP 4: Generate Buy Sell Indicators and Do Backtest
Now we should use it to generate buy sell indicators. This should follow some specific rules based on different strategies.
(I have only built in one strategy rule called momentum
. In fact, other strategies can be add inside the package easily.)
Call backtest_Momentum
from class backtest
, then you will get a return as matrix (two-dimensional array).
The buy sell indicator is in 4th column. We can use it to do backtest by calling get_PnL
.
backtest_Momentum = backtest.backtest_Momentum(data_PS=data_PS, data_CT=data_CT.bar_idx_live)
print (backtest_Momentum) # see the return matrix
result = backtest.get_PnL(close=data_60.close, indicator=backtest_Momentum[:,2], cost=0.15/100, sharpe_frequency=365*2, beta=1)
print (result) # see the backtest result
STEP 5: Plot the Backtest Result
This step will plot a custom image based on the backtest result. Note that I only built-in one kind of custom image.
plot1 = plotting.plot_type1(Open=data_60.open, High=data_60.high, Low=data_60.low, Close=data_60.close, indicator=backtest_Momentum[:,2], result=result)
Author
He Dekun
License
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for hdk_pkg_critest-0.5.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1041ed29aed95655dd809daad5a0ab631ef850c83a6c36515f267de9c029c8b7 |
|
MD5 | 9bfed3ea0f28843ba990e60095662372 |
|
BLAKE2b-256 | 7e4151ce8bc5fd0df1a2c852a7125b8627b0c150da7374514c6b70e09c7877a2 |