With the use of technical indicators like the RSI (Relative Strength Index), Bollinger bands, MACD, etc., technical analysis on financial data is frequently performed using the open-source library known as TA-Lib, or Technical Analysis Library. It works with C/C++, Java, Perl, and other programming languages in addition to Python. The following are some of the TA-features: Lib's Bollinger Bands, Aroon Oscillator, Moving Average Convergence/Divergence, and Relative Strength Index are all abbreviations for the same thing: BBANDS. Read more here about similar operations.
Trading Libraries in Python for Data Manipulation
Large multi-dimensional arrays and matrices may be implemented effectively with NumPy, often known as Numerical Python. The library includes utilities for handling sophisticated arrays and performing advanced computations on them. Trigonometric functions (sin, cos, tan, radians), hyperbolic functions (sinh, cosh, tanh), logarithmic functions (log, logaddexp, log10, log2), and other mathematical operations are some of the mathematical operations available in this library.
Pandas is a sizable Python library used for data analysis and manipulation as well as working with numerical tables, data frames, and time series; as a result, Pandas is widely utilised in Python-based algorithmic trading. It is possible to use Pandas for a variety of tasks, such as importing.csv files, carrying out arithmetic operations in series, boolean indexing, gathering details about a data frame, etc.
SciPy is an open-source Python library for scientific calculations, as the name would imply. To carry out sophisticated operations like numerical integration, optimization, image processing, etc., it is used in conjunction with NumPy. Several SciPy modules, including scipy.integrate (for numerical integration), scipy.signal (for signal processing), and scipy.fftpack (for fast fourier transform), are utilised to carry out the aforementioned operations.
Trading Library for Plotting Structures in Python.
This Python module is used to plot 2D structures like graphs, charts, histograms, scatter plots, and so on. It becomes required to utilise matplotlib to portray that data in a graphical way using charts and graphs in addition to the other libraries that are needed for calculations. Matplotlib has a number of useful functions, such as scatter (for scatter plots), pie (for pie charts), stackplot (for stacked area plots), colorbar (to add a colorbar to the plot), and others.
Python Trading Libraries for Machine Learning
It is a machine learning library based on the SciPy library that includes a variety of algorithms for classification, clustering, and regression. It may be used in conjunction with other Python libraries like NumPy and SciPy for mathematical and scientific calculations. Sklearn.cluster, Sklearn.datasets, Sklearn.ensemble, Sklearn.mixture, etc. are a few of its classes and functions. More information on the library and its purposes may be found here.
An open source software library called TensorFlow is used for machine learning applications including neural networks and high speed numerical computations. Due to its adaptable design, compute may be easily deployed across a range of platforms, including CPUs, GPUs, and TPUs. Find out here how to install TensorFlow GPU.
Neural networks and other deep learning models are created using the Keras deep learning package. It focuses on being modular and adaptable and can be built on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano. It includes the components needed to construct neural networks, including layers, goals, optimizers, etc. This example instals Keras on Python and R. This library may be used in trading for artificial neural network-based stock price prediction.
Python Trading Libraries for Backtesting
a backtesting-focused event-driven library that supports both paper trading and live trading. PyAlgoTrade enables you to quickly analyse the behaviour of your trading ideas using historical data. supports access to data from NinjaTrader CSVs, Yahoo Finance, Google Finance, and any sort of time series data in CSV. it also supports event-driven backtesting. It supports TA-Lib integration and has comprehensive documentation (Technical Analysis Library). Although it is faster and more adaptable than other libraries, its greatest flaw is that it doesn't support the Pandas-object and pandas modules.
Both live trading and backtesting are supported by this event-driven system. Zipline is well-documented, has a wonderful community, and enables the integration of Interactive Broker and Pandas. However, Zipline is less practical for trading many goods and slower than commercial systems with backtesting features in a built programme.
Python/pandas vectorized backtesting framework created to make backtesting small, easy, and quick. While masking all manual calculations for trades, equity, performance data, and visualisation creation, it enables the user to describe trading strategies while utilising the full potential of pandas. The final strategy code may be used in both testing and production settings. Multi-security testing might be accomplished by first executing single-sec backtests and then combining equity. Currently, only supports single security backtesting. It is still being worked on to include multi-asset backtesting capabilities.
Python Trading Libraries for Data Collection
The system is vectorized. a Python project for gathering, evaluating, and backtesting trading strategy data in real-time. supports data access via Excel, HBade, Google Finance, Yahoo Finance, and Google Finance.
TWP (Trading With Python)
TWP library, sometimes known as TradingWithPython, is another vectorized system. It is an assortment of classes and functions for quantitative trading. It has frequently employed P&L benchmarking features as well as tools for obtaining data from sources including Yahoo Finance, CBOE, and Interactive Brokers. However, the training and documentation for this library are $395.
Trading on Interactive Brokers using Python
Python is one of the many computer languages that may be used on the trading platform provided by Interactive Brokers, an online broker. For a comprehensive range of electronically traded goods, including stocks, options, futures, currencies, bonds, CFDs, and funds, it offers access to more than 100 global market locations. IB offers a very straightforward and user-friendly interface in addition to highly competitive commission and margin prices. We will go through how to connect to IB using Python in this section.
There are a few intriguing Python libraries that can be used to connect to live markets using IB. In order to use these libraries to trade with real money, you must first have an account with IB.
The Python library is user-friendly and adaptable, and it may be used to trade with Interactive Brokers. It is an API wrapper for IB that offers a highly user-friendly solution while masking IB's complexity. You may watch this YouTube video or read this awesome blog to learn how to use the IBridgePy library.
A other Python library that may be used to trade with Interactive Brokers is IBPy. You may find information about setting up and using IBPy here. Each library has unique strengths and disadvantages, as was already established. After assessing the benefits and drawbacks, you may select the best library based on the requirements of the plan. We have examined many libraries up to this point; now, let's move on to Python trading platforms.