dxlib - a Quantitative Analysis Framework

dxlib offers a rich set of tools tailored for traders, researchers, and quantitative analysts, covering everything from basic statistical methods to comprehensive trading simulations. It emphasizes a unified interface for fetching and analyzing data, ensuring consistent and predictable outputs irrespective of data sources.

Installation

Use the package manager pip to install dxlib.

pip install dxlib

Modules Overview

Interfaces

Encapsulates a series of financial data api, allowing static data requests, data streaming and websocket creation. Currently available APIs endpoints are:

• Alpaca Markets
• Alpha Vantage
• Yahoo! Finance

Unified data-fetching methods, ensuring consistent data structures. To use the individual API endpoints, simply load the respective API module:

from dxlib.interfaces.api import YFinanceAPI

api = YFinanceAPI()

top_symbols = api.get_symbols(n=100)
historical_bars = api.historical_prices(top_symbols["symbol"].values)

print(top_symbols)
print(historical_bars)

   symbol    trade_count        volume
0    MCOM          86713     146651863
1    EBET          33428      83560053
...
98   FNGD           9886       5580585
99   OPEN          13278       5528415

Fields                  Close                   ...       VWAP      Volume            
Ticker                  AAL      AAPL   ABCM    ...       XPEV         XLF        XPEV
Time                                            ...                                   
2022-08-29 04:00:00     13.51  160.43  14.99    ... 
...
2023-08-25 04:00:00     NaN      NaN     NaN    ...  17.149326  37520857.0  19358445.0

Simulation Module

Harness dxlib to simulate trading strategies:

import dxlib as dx
from dxlib import StrategyManager, Portfolio
from dxlib.interfaces.api import YFinanceAPI

data = YFinanceAPI().get_historical_bars(["AAPL", "MSFT", "GOOGL", "AMZN"], start="2022-01-01", end="2022-12-31")
portfolio = Portfolio().add_cash(1e4)
my_strategy = StrategyManager(dx.strategies.RsiStrategy())
my_strategy.register(portfolio)
historical_signals = my_strategy.run(data["Close"])

Highlight: Built-in Server

The simulation manager features an embedded server that serves requests related to the simulation process. This allows for real-time monitoring and potential interaction with running simulations. Check out the Server class in the simulation module for more details.

Start the server with simulation_manager.start_server(). Check the logs for server activity and exception handling.

import dxlib as dx
from dxlib.interfaces.servers import SimulationManager

data = dx.api.YFinanceAPI().get_historical_bars(["AAPL", "MSFT", "GOOGL", "AMZN"], start="2022-01-01", end="2022-12-31")
my_strategy = dx.strategies.RsiStrategy()

logger = dx.info_logger("simulation_manager")
my_manager = SimulationManager(my_strategy, use_server=True, port=5000, logger=logger)

my_manager.start()

try:
  while my_manager.server.is_alive():
    with my_manager.server.exceptions as exceptions:
      if exceptions:
        logger.exception(exceptions)
except KeyboardInterrupt:
  pass
finally:
  my_manager.stop_server()

$ python servers.py
2023-08-15 04:11:37,417 - INFO - Server starting on port 5000 (http_server.py:308)
2023-08-15 04:11:37,422 - INFO - Server started on port 5000. Press Ctrl+C to stop (http_server.py:292)
127.0.0.1 - - [15/Aug/2023 04:12:01] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [15/Aug/2023 04:12:09] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [15/Aug/2023 04:13:35] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [15/Aug/2023 04:14:18] "POST /add_cash HTTP/1.1" 200 -
127.0.0.1 - - [15/Aug/2023 04:15:38] "GET /portfolio HTTP/1.1" 200 -

Trading Module

from dxlib.strategies import RandomForestStrategy as rfs
from dxlib.data import DataLoader

features, labels = DataLoader(data["Close"])
x_train, y_train, x_test, y_test = train_test_split(features, labels, 0.5)
clf = rfs().fit(x_train, y_train)
y_pred = rfs.predict(x_test)
print(f"Returns: {sum(y_pred - y_test}")

History Class

Description:
The History class is a robust stock market data handler, offering a suite of utilities and technical indicators for market analysis.

• Data Management: Seamlessly manage your stock price data. Easily add new symbols or rows of data.

• Timeseries Indicators: Common indicators and timeseries methods:

  • Moving Averages
  • Exponential Moving Averages
  • Logarithmic Changes
  • Volatility Measurements

• Technical Indicators: Enjoy a rich suite of built-in technical indicators, such as (Convert your History to JSON format with a single call to the to_json method):

  • RSI
  • Sharpe Ratio
  • Bollinger Bands
  • Beta
  • Drawdown Computations

• Extendability: Extend the History class with more technical indicators or data analysis tools as per your requirements.

Here's a quick start guide to using the History class:

from dxlib import History
import pandas as pd

# Sample data
data = pd.DataFrame({
    'AAPL': [150, 151, 152],
    'MSFT': [300, 301, 302]
})

history = History(data)

# Adding a new stock ticker
history.add_security('TSLA', [650, 651, 652])

# Calculating moving averages
moving_average = history.indicators.sma(window=2)
print(moving_average)

Contributions

We welcome improvements and extensions:

1. Fork the repository.
2. Make your enhancements.
3. Submit a pull request.

Ensure thorough testing of all changes. Your contributions will make dxlib even better!