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filib-0.6.0
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Factor Investing Library
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | filib-0.6.0 |
| نام | filib |
| نسخه کتابخانه | 0.6.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Paweł Kowalski |
| ایمیل نویسنده | prm.kowalski@gmail.com |
| آدرس صفحه اصلی | https://github.com/prmkowalski/filib |
| آدرس اینترنتی | https://pypi.org/project/filib/ |
| مجوز | Apache License 2.0 |
filib
=====
.. image:: https://img.shields.io/pypi/pyversions/filib
:target: https://pypi.org/project/filib/
:alt: PyPI - Python Version
.. image:: https://img.shields.io/pypi/v/filib
:target: https://pypi.org/project/filib/
:alt: PyPI
.. image:: https://img.shields.io/pypi/status/filib
:target: https://pypi.org/project/filib/
:alt: PyPI - Status
.. image:: https://img.shields.io/github/license/prmkowalski/filib
:target: https://github.com/prmkowalski/filib/blob/master/LICENSE
:alt: GitHub
.. image:: https://github.com/prmkowalski/filib/workflows/CI/badge.svg
:target: https://github.com/prmkowalski/filib/actions?query=workflow%3ACI+branch%3Amaster
:alt: CI - Status
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/psf/black
:alt: Black
`Factor Investing <https://en.wikipedia.org/wiki/Factor_investing>`_
LIBrary is a lightweight algorithmic trading Python library built for easy testing of
predictive factors and portfolio rebalance via
`Oanda <https://developer.oanda.com/rest-live-v20/introduction/>`_.
Inspired by and compatible with
`Quantopian Open Source <https://github.com/quantopian>`_.
**NOTE**: This library is currently in alpha stage. Until it becomes stable
I strongly recommend using practice account for testing and trading. You can also
expect major changes without warnings, mostly responses to
`Issues <https://github.com/prmkowalski/filib/issues>`_.
`Changelog » <https://github.com/prmkowalski/filib/releases>`_
Installation
------------
Install with `pip <https://pip.pypa.io/en/stable/>`_:
.. code:: bash
$ pip install filib
Usage
-----
Proposed workflow contains three steps. Here's an example:
1. Assemble
^^^^^^^^^^^
Begin with imports, create hypotheses and write functions with predictive factors:
.. code:: python
from filib.oanda import Oanda # Currently only Oanda FOREX is available
from filib.helpers import * # Optional, useful for factor generation
class MyFactors(Oanda):
def momentum(self): # THEORY: Persistence in asset performance
factor = self.returns # Write down your factor formula
return factor # By default split factor data to 3 quantiles
@swap_sign # Short high and long low factor values
def relative_strenght_index(self): # THEORY: Oversold / overbought indicator
factor = rsi(self.close, 14)
split = [0, 30, 70, 100] # List of thresholds or int to split equally
return factor, split # Follow this order: factor, split
def big_mac_index(self): # THEORY: Simplified Purchasing Power Parity
import quandl # Financial, Economic and Alternative Data
iso_codes = get_iso_codes(self.price_data)
codes = [f"ECONOMIST/BIGMAC_{COUNTRY}.5" for COUNTRY in iso_codes]
factor = quandl.get(codes).dropna(how="all", axis=1)
factor.columns = [iso_codes[c.split("_")[1].split()[0]] for c in factor]
factor.index = factor.index.tz_localize("UTC") # Convert time zone to UTC
return factor
2. Research
^^^^^^^^^^^
Initialize parameters (during the first run you will be asked to provide credentials):
.. code:: python
model = MyFactors(
instruments=["EUR_USD", "GBP_USD", "USD_JPY", "AUD_USD", "NZD_USD", "USD_CAD",
"USD_CHF", "USD_NOK", "USD_SEK"], # Define universe
symbol="USD", # Optional, specify symbol to arrange price data
granularity="D", # Time period between each candle and between each rebalance
count=500, # Number of historical OHLCV candles to return for analysis
periods=(1, 2, 3), # Optional, specify periods for factor decay analysis
split=3, # Number of quantiles to split combined factor data
long_short=True, # Trade only top and bottom factor quantile
combination="sum_of_weights", # Formula for combining factors together
leverage=3, # Multiplier for the portfolio positions
)
Check the performance of factors combined together:
.. code::
>>> model.performance()
Collecting price data: |██████████████████████████████| 9/9 [100%] in 4.0 s
Preparing factor data: |██████████████████████████████| 3/3 [100%] in 12.0 s
MyFactors - INFO - Factor `MyFactors_combined` Analytics:
Min Max Mean Size Returns (bps)
factor factor factor factor 1D 2D 3D
factor_quantile
1.0 -1.003 0.000 -0.237 1499 -1.337 -2.068 -2.320
2.0 -0.243 0.210 0.005 1461 -2.582 -3.299 -5.138
3.0 -0.027 0.973 0.238 1459 0.892 -0.835 -2.266
1D 2D 3D
- Information Coefficient: 0.037 0.001 0.0
- Factor Rank Autocorrelation: 0.05
- Annualized Sharpe Ratio: 0.76
- Annualized Alpha (Beta): 0.080 (0.042)
- Win Rate: 52.55%
- Risk / Reward: 1.02
- Profit Factor: 1.15
- Start Date: 2018-07-11
- End Date: 2020-05-27
- Duration: 686 days 00:00:00 (1.9 years)
- Rebalance every: 1D
- Compound Annual Growth Rate: 7.78%
- Annualized Volatility: 10.44%
- Maximum Drawdown: -11.49%
- Maximum Drawdown Duration: 434 days 00:00:00
...
Alternatively set selection rules with a
`query <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html>`_
string to evaluate. Available metrics:
- **ic**: Information Coefficient based on Spearman Rank Correlation
- **autocorr**: Factor Rank Autocorrelation
- **sharpe**: Annualized Sharpe Ratio
- **beta**: Annualized Beta as exposure to trading universe
- **alpha**: Annualized Alpha as excess returns over trading universe
- **win**: Win Rate
- **rr**: Risk / Reward Ratio
- **profit**: Profit Factor = (sum of earnings) / (sum of losses)
- **cagr**: Compound Annual Growth Rate
Then analyze the performance of individual factors and select those that meet the rules:
.. code::
>>> model.select(
... rules="abs(ic) > .01 or profit > 1", # Example query expression
... swap_to="cagr", # Align the signs of selected factors to specified metric
... inplace=True, # Modify model to contain only selected factors
... )
Preparing performance: |██████████████████████████████| 3/3 [100%] in 6.2 s
MyFactors - INFO - Factors with signs that meet the rules `abs(ic) > .01 or profit > 1`:
big_mac_index -1.0
momentum 1.0
relative_strenght_index 1.0
3. Trade
^^^^^^^^
Check portfolio positions based on selected factors and generated submitted orders:
**PLEASE USE AT YOUR OWN RISK - THIS CAN TRADE REAL MONEY - NO WARRANTY IS GIVEN**
.. code::
>>> model.rebalance(
... accountID="", # Your Oanda Account Identifier
... live=True, # Actually place orders
... )
MyFactors - INFO - Portfolio from `2020-05-28 00:00:00+00:00`:
NOK -19.5%
SEK -15.3%
CHF -15.2%
AUD 0.0%
EUR 0.0%
GBP 0.0%
NZD 9.0%
CAD 15.3%
JPY 25.8%
- Account NAV: 8423.77 EUR
- Position Value: 25382.12
- Submitted Orders:
USD_JPY -7240
NZD_USD 4050
USD_CAD -4280
USD_CHF 4260
USD_NOK 5490
USD_SEK 4280
Contributing
------------
Pull requests are welcome. For major changes, please open an issue first to discuss
what you would like to change.
نیازمندی
| مقدار | نام |
|---|---|
| <1.4 | pandas |
| >=5.0.2 | coverage[toml] |
| - | matplotlib |
| - | pytest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl filib-0.6.0:
pip install filib-0.6.0.whl
نصب پکیج tar.gz filib-0.6.0:
pip install filib-0.6.0.tar.gz