Algorithmic trading, often referred to as algo-trading, harnesses computer programs to automate the process of buying and selling securities based on predefined criteria. By eliminating human emotions and allowing for rapid execution of trades at large scales, it has fundamentally transformed the financial world, accounting for a significant portion of daily trading volume on major stock exchanges.

At the heart of this revolution is Python, an open-source programming language known for its simplicity, versatility, and rich ecosystem. Over the past decade, Python has cemented its place as a premier tool in the finance sector, particularly in algorithmic trading. Its rise can be attributed to several factors:

The combination of Python's innate strengths, along with the specialized tools developed by its vast community, makes it the prime choice for those venturing into the dynamic world of algorithmic trading.

From the days of traders shouting buy and sell orders on the stock exchange floor to the modern age of algorithmic trading, the tools of the trade have undergone monumental transformations. In the earlier days, trading was more intuition-based, often relying on manually plotted charts and fundamental analysis. With the advent of computers in the 1980s, quantitative and algorithmic trading began to take root, leading to a demand for programming languages that could handle complex mathematical models and large datasets efficiently.

In the quest for the ideal programming language for trading, various contenders emerged. C++ was favored for its execution speed, making it a preferred choice for high-frequency trading[1]. Java, with its Write Once Run Anywhere (WORA) capabilities, became popular among trading firms looking for a platform-independent solution. R, with its suite of statistical tools, attracted those focused on data analysis and research.

However, Python, despite being a general-purpose language, began to gain traction in the financial world. Several reasons contributed to this:

Real-world adoption of Python in trading is rampant. Renaissance Technologies, one of the most successful hedge funds, is known to use Python for its data analysis and algorithmic trading strategies[2]. Two Sigma, another giant in the hedge fund world, heavily uses Python for its data-heavy approach to investing. Additionally, individual traders and smaller firms have shared numerous success stories, attributing their profitability and efficiency to Python's capabilities.

In conclusion, while each language has its merits and specific use cases, Python's versatility, coupled with its active community and vast library ecosystem, makes it a front-runner in the world of algorithmic trading.

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Entering the world of algorithmic trading with Python might seem daunting at first, but fear not. Whether you're a seasoned coder or someone with limited programming experience, Python's versatility makes it an excellent choice for trading.

The first step to using Python in trading is ensuring you have the right setup. Here's a step-by-step guide to help you kick off your trading journey:

Installation and Setup for Various Platforms

Remember, while Python 2 is still in use in some systems, Python 3 is the newer version and is widely recommended for trading due to its advanced features and extensive support.

Best Practices for Setting Up a Python Environment Tailored for Trading

Establishing a tailored Python environment for trading ensures that your projects remain organized and free from package conflicts. Here are some best practices:

Incorporating Python into your trading activities can revolutionize your approach, allowing for more complex strategies and analysis to be conducted with ease. With the right setup and environment, you'll be poised to take full advantage of what Python offers in the trading realm. Now, with your environment set up, you're ready to dive into the wide array of libraries and tools available to Python traders.

The financial world generates vast amounts of data every second. Properly fetching and managing this data is the cornerstone of any quantitative trading strategy. Python, with its extensive range of libraries, offers traders versatile tools to retrieve and manipulate data efficiently.

pandas

yfinance

ccxt

alpha_vantage

quandl

websocket

By utilizing these libraries, traders can equip themselves to tackle the vast sea of financial data and harness it for profitable strategies.

In the world of trading, technical and fundamental analyses serve as the backbone of most trading strategies. Python, with its vast ecosystem of libraries, provides essential tools to carry out these analyses seamlessly.

TA-Lib (TA-Lib.org):

pandas-datareader (GitHub Repo):

fmpsdk (GitHub Repo):

fastquant (GitHub Repo):

Incorporating these libraries into your trading strategy not only expedites the analysis process but also ensures accuracy and consistency. The choice of library often depends on the specific requirements of the trading strategy, data availability, and personal preference. Whether you're delving into simple moving average crossovers or complex financial metrics, Python's ecosystem has you covered.

In the ever-evolving landscape of quantitative finance, machine learning stands at the forefront, offering tools and techniques to extract patterns and make predictions. Python, given its rich ecosystem, has become the go-to language for implementing machine learning in trading strategies.

scikit-learn

TensorFlow and Keras

LightGBM

Prophet

Machine learning's application in trading is vast, and the libraries listed above only scratch the surface. Whether you're predicting stock prices, analyzing market sentiment, or looking for patterns in vast datasets, Python provides the tools you need. As always, while these tools offer immense potential, it's crucial to understand the underlying algorithms, biases, and potential pitfalls. Proper validation and out-of-sample testing are imperative to avoid overfitting and to build robust trading strategies.

Backtesting is a critical process in the life cycle of algorithmic trading. It simulates a trading strategy on historical data to evaluate its effectiveness before deploying it with real capital. Alongside backtesting, strategy evaluation determines the strategy's risk and potential returns, offering quantitative traders insights on its viability. In the world of Python trading, several libraries empower traders to perform these tasks with efficiency and precision.

Backtrader

pyfolio

QuantConnect

Fastquant

Quantlib

To summarize, choosing the right backtesting and strategy evaluation library depends on your requirements. For a holistic approach, Backtrader serves most needs. If performance analysis is paramount, pyfolio is unparalleled. Platforms like QuantConnect provide a seamless transition from backtesting to live trading, while Fastquant is perfect for beginners. Evaluate your needs, and you'll find Python provides a tool tailored for it.

Visualization is an essential component of trading. It provides a way to interpret vast amounts of data, discover patterns, and make informed decisions. Reporting, on the other hand, helps in maintaining a structured record of trading activities, performance, and insights. Python offers several libraries for both visualization and reporting that cater to trading needs.

Matplotlib[10]

Pandas[11]

Seaborn[12]

Plotly[13]

pyfolio[14]

By leveraging these libraries, traders can create insightful visualizations to better interpret data and make informed trading decisions. Furthermore, with the reporting functionalities, traders can maintain a clear record and evaluate the performance of their strategies over time.

Data Fetching

In a basic momentum strategy, we bet that assets that have performed well in the past will continue to perform well in the future. To fetch historical stock price data, we can use the yfinance library.

Data Preprocessing

The preprocessing step involves computing the momentum indicator, which is usually a simple rate of return over a set period.

Strategy Formulation

To implement the momentum strategy, we create a signal indicating whether to buy or sell.

Backtesting

Backtesting evaluates how the strategy would have performed historically. For simplicity, let's assume that we invest $1 at the start.

Optimization

Optimization involves fine-tuning parameters such as the period over which momentum is calculated. This can be achieved using grid search or more advanced optimization techniques.

Data fetching

Mean-reversion is based on the assumption that asset prices and historical returns eventually revert back to their long-term mean or average. The first step in creating a mean-reversion strategy is to fetch historical price data. We can use the yfinance library in Python to fetch this data for a given asset:

Data preprocessing

Once data is fetched, it's crucial to prepare and clean it. For a mean-reversion strategy, we might be interested in computing a moving average and standard deviation. Pandas can help with these tasks:

Strategy formulation

With a mean-reversion strategy, we typically buy assets when they're below their historical average and sell when they're above. One common approach is to use z-scores:

Backtesting

Backtesting is critical to understand how the strategy would've performed historically. We'll keep it simple here using backtrader:

Optimization

Optimization helps in tweaking parameters to potentially improve performance. Using backtrader, we can optimize our moving average window size:

Select the window size that gives the best risk-adjusted return.

Data Fetching

Machine learning in trading heavily relies on large volumes of historical data to train models. Python's ecosystem provides various libraries for fetching financial data. One such library is yfinance:

This code will retrieve historical stock prices for Apple Inc. from 2010 to 2023. You can easily adjust the ticker symbol and date range to fetch data for different assets or periods.

Data Preprocessing

Once data is fetched, it's essential to preprocess it for machine learning models. Common preprocessing steps include normalization, handling missing values, and feature engineering.

Strategy Formulation

Using a simple predictive model like Linear Regression, we can try to predict future stock prices:

Backtesting

For evaluating the performance of our trading strategy, we need to backtest it against historical data. Backtesting involves simulating trades based on our model's predictions and assessing the performance.

Optimization

A simple optimization technique involves hyperparameter tuning for the machine learning model. For more complex models like Random Forest or Gradient Boosted Trees, libraries like GridSearchCV can be used:

This code will tune a Random Forest model by searching over the specified hyperparameters.

Note: All these code snippets serve as basic examples to illustrate concepts. In practice, more sophisticated and thorough methodologies would be necessary for robust trading strategies.

Data Fetching

Sentiment analysis trading relies heavily on data generated from news articles, social media, and other text-rich sources. Using Python, one can fetch this data conveniently:

Example using requests and BeautifulSoup:

This example fetches the latest headlines from a hypothetical financial news website.

Data Preprocessing

Before applying sentiment analysis, the fetched data needs preprocessing to improve accuracy:

Example using NLTK:

This script tokenizes the headline and removes common stopwords.

Strategy Formulation

Based on sentiment scores, one can decide whether to buy, sell, or hold:

Example using TextBlob:

In this basic strategy, if sentiment polarity is above 0.5, it suggests buying, below -0.5 suggests selling, and in between suggests holding.

Backtesting

Backtesting involves applying the sentiment-based strategy on historical data to evaluate its efficacy:

Optimization

Optimizing involves fine-tuning thresholds or utilizing more advanced models:

To further optimize, traders can explore advanced models like BERT or transformers for sentiment analysis[15]. Additionally, integrating other indicators with sentiment scores can enhance strategy performance.

Time series analysis is a fundamental component in the world of quantitative trading. Given the sequential nature of financial data, tools tailored for time series forecasting can be indispensable. Among these, ARIMA (AutoRegressive Integrated Moving Average) and GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) are two prominent models known for their efficiency in modeling and forecasting financial time series data.

ARIMA (AutoRegressive Integrated Moving Average)

ARIMA is a popular statistical method for modeling and forecasting time series data. It combines three components:

For predicting financial time series, ARIMA can be set using combinations of these components. The (p,d,q) order in the model signifies the number of AR terms, differencing, and MA terms, respectively.

GARCH (Generalized AutoRegressive Conditional Heteroskedasticity)

Volatility clustering is a common phenomenon in financial markets where periods of low volatility are followed by periods of high volatility and vice versa. GARCH models are designed to handle such situations by modeling the changing variance over time.

A GARCH(p,q) model is specified by the order (p, q), where p is the number of GARCH terms (lag variance) and q is the number of ARCH terms (lag squared returns).

In trading, the GARCH model can be used to forecast future volatility, which can be critical for strategies that depend on volatility forecasts, such as options trading.

Combining ARIMA and GARCH

Often, ARIMA and GARCH models can be combined to create a more robust forecasting mechanism, where ARIMA is used for mean equation and GARCH for volatility equation.

When employing time series models like ARIMA and GARCH, it's essential to validate model assumptions, check for model stability, and ensure that residuals do not exhibit patterns (i.e., residuals are white noise).

Both ARIMA and GARCH have been time-tested in the financial industry. They can serve as starting points for traders diving into time series forecasting. However, always keep in mind that no single model provides a silver bullet. Regularly re-evaluate, backtest, and adjust based on changing market conditions.

In the realm of quantitative trading, deep learning has opened new avenues for predicting financial market movements with enhanced accuracy. At its core, deep learning is a subset of machine learning where algorithms are inspired by the structure and function of the brain called artificial neural networks.

Neural Networks in Trading: Neural networks, especially recurrent neural networks (RNN) and their advanced version, long short-term memory (LSTM) networks, have become popular for stock price prediction. These networks are adept at handling sequences, making them suitable for time series data like stock prices. For instance, an LSTM can be trained to recognize patterns over time and predict future price points based on historical data.

Feature Engineering: Unlike traditional time series models, deep learning thrives on high dimensional data. This means traders can incorporate diverse data sources like trade volume, order book data, or even macroeconomic indicators. However, this strength also means that feature engineering - the process of selecting the right input variables - becomes crucial. Too much noise can impede model performance, while the right features can enhance predictability.

Hyperparameter Tuning: Training a deep learning model isn't just a one-off task. Traders must engage in hyperparameter tuning, adjusting aspects like learning rates, dropout rates, or the number of layers in the neural network, to optimize the model's predictive power[16].

Overfitting Concerns: One of the challenges of deep learning in trading is overfitting, where a model performs exceptionally well on training data but poorly in real-world trading. Regularization techniques, early stopping, or even ensemble methods can help mitigate this risk.

Real-time Adaptability: Financial markets are dynamic. Thus, for a deep learning model to remain relevant, it should be retrained periodically with fresh data or even be designed to adapt in real-time.

Potential and Limitations: While deep learning offers improved accuracy over traditional models, it's not a silver bullet. The non-linear nature of financial markets, influenced by a multitude of unpredictable factors, means there will always be a degree of uncertainty. Traders should use deep learning as a tool among others, combining its insights with other analysis methods for a holistic trading strategy.

For those keen on diving into the practical side, popular Python libraries like TensorFlow and Keras offer robust platforms for building and training deep learning models tailored for price prediction.

Remember, while deep learning brings advanced capabilities to the table, it also demands a rigorous approach to model training, validation, and evaluation. As with all trading strategies, it's essential to be aware of the risks and ensure that the strategy aligns with individual trading goals and risk appetite.

Reinforcement learning (RL) has made significant inroads into the world of quantitative trading, offering a framework wherein trading agents learn optimal strategies by interacting with financial markets. RL differs from traditional machine learning approaches in that it focuses on making a sequence of decisions by discovering which actions yield the most reward over time.

The basic premise in RL-based trading is that an agent, such as a trading algorithm, takes actions, like buying or selling an asset, within an environment, which in this case is the financial market. The agent then receives feedback in the form of a reward or penalty based on the success of its actions, allowing it to adjust its future actions accordingly.

Key Advantages:

Q-Learning and Deep Q Networks (DQN): One of the popular RL methods applied to trading is Q-learning, which estimates the expected reward for each action in a given state. When combined with deep learning, Deep Q Networks (DQN) can handle large state spaces, making them suitable for trading in multiple assets or incorporating various market indicators.

Policy Gradient Methods: Another class of RL techniques, policy gradient methods, directly optimize the trading policy. They can cater to continuous action spaces, making them suitable for trading problems where the action might be the proportion of a portfolio invested in an asset.

Limitations: However, there are challenges. RL methods require a lot of data and computational power. The noisy and non-stationary nature of financial markets can sometimes make the learning process unstable. Moreover, RL strategies might overfit to certain market conditions and might not generalize well to unseen scenarios.

Practical Considerations:

In conclusion, while reinforcement learning holds promise for developing adaptive and dynamic trading strategies, a careful and holistic approach that considers market nuances, transaction costs, and the potential pitfalls of overfitting is essential. With continuous advancements in RL algorithms and computational power, the application of RL in trading is poised to grow.

Portfolio optimization is the process of selecting the best portfolio out of a set of portfolios according to some objective. The objective typically maximizes factors like expected return and minimizes costs like risk. Python, given its rich ecosystem, provides powerful tools to quantitatively and qualitatively analyze and optimize portfolios.

Mean-Variance Optimization (MVO)

Historically, the most common method for portfolio optimization has been Markowitz's Mean-Variance Optimization. It seeks to maximize expected portfolio returns for a given amount of risk or minimize risk for a given level of expected returns.

Black-Litterman Model

While MVO focuses solely on the quantitative side, the Black-Litterman model integrates both subjective views on asset performance and the market equilibrium. By blending these, it offers more intuitive, stable portfolio weights.

The Python library PyPortfolioOpt offers an efficient implementation of this model.

Hierarchical Risk Parity (HRP)

HRP doesn't require an estimate of expected returns, making it free from the errors of such predictions. It builds on the hierarchical clustering of assets based on the similarity of their price series.

Constraint Optimization

There are times when investors have specific constraints, such as maximum allocation to a specific sector or minimum allocation to certain assets. Python's optimization libraries like scipy can handle these constraints effectively.

All these methods provide different perspectives and often yield different results. It's crucial to understand the underlying assumptions and trade-offs each method offers. For a practitioner, it's not uncommon to blend insights from various techniques for a comprehensive portfolio strategy.

Lastly, it's essential to remember that optimization techniques can be sensitive to the input data. Therefore, ensuring data quality, considering transaction costs, and accounting for model assumptions are paramount for the success of any portfolio optimization technique[17].

Python's flexibility and extensiveness make it a popular choice for integrating with various trading platforms, enabling traders to automate their strategies and capitalize on market opportunities efficiently. Here, we'll explore how to seamlessly connect Python scripts with real-world trading platforms and delve into the nuances of API connections, web sockets, and handling real-time data.

Most established trading platforms and brokers offer Application Programming Interfaces (APIs) that allow developers to interface with their platforms programmatically. For instance, Interactive Brokers, one of the leading brokers globally, provides its TWS API[18], through which Python scripts can fetch market data, place trades, and access account information.

To work with such APIs, you typically need to:

For traders looking to capitalize on minute-by-minute market fluctuations, it's paramount to have access to real-time data. While traditional HTTP-based APIs are suitable for occasional data fetches, they're not ideal for real-time streaming. Enter web sockets.

Web sockets provide a persistent, low-latency connection between the client and server, making them ideal for real-time data streaming. Many trading platforms, like Binance[20] for cryptocurrency trading, provide web socket endpoints. Python libraries such as websocket or websockets can be used to establish and maintain these connections.

Here's a simple flow:

Efficiently handling the incoming data is crucial to ensure timely trade execution and strategy assessment. Here are some best practices:

To sum up, integrating Python with trading platforms is a multifaceted process that, when executed correctly, can automate and optimize trading operations significantly. While this overview provides a foundation, it's essential to consult the specific documentation of your chosen trading platform and related Python libraries for detailed integration steps.

Effective risk management is crucial for preserving capital and ensuring longevity in trading. Python, due to its versatility, can be instrumental in automating and optimizing these processes. Here’s how:

Setting Stop-Loss and Take-Profit Levels with Python

The stop-loss and take-profit levels are fundamental components of risk management. They help traders lock in profits and limit potential losses.

A simple method to set a stop-loss in Python is by determining a fixed percentage of the asset's price. For example, if you decide on a 2% stop-loss on a stock trading at $100, the stop-loss would be set at $98.

Similarly, for a take-profit, you can determine a desired percentage gain:

Evaluating Trading Strategy Performance

Merely looking at the returns of a strategy is not sufficient. Comprehensive evaluation requires considering various metrics to understand both the returns and associated risks.

Sharpe Ratio: The Sharpe ratio measures the risk-adjusted return of an investment. A higher Sharpe ratio indicates better performance on a risk-adjusted basis.

Drawdown: Drawdown measures the largest decline from a peak in the value of a portfolio. It gives an idea of the downside risk over a specified time period.

In conclusion, Python is an exceptional tool for setting automated risk parameters and evaluating the performance of a trading strategy. By integrating these metrics and techniques into your trading routine, you can make more informed decisions, manage risks effectively, and have a clear understanding of your strategy's strengths and weaknesses.

Markets are dynamic entities, constantly evolving and responding to a myriad of factors, from macroeconomic changes to geopolitical events to technological innovations. For quantitative traders, a strategy that worked last year, or even last month, might not necessarily work today. This fluidity underscores the critical importance of continuous learning and adaptability.

Adapting to market changes is not just about responding to shifts in market sentiment or macroeconomic events. It involves staying current with the latest research, understanding new data sources, and being able to incorporate the latest techniques and technologies into one's trading strategies. A static approach in a dynamic market can lead to deteriorated performance or increased risk exposure.

Iterative testing and strategy refinement are vital tools in a trader's arsenal. This involves routinely evaluating the performance of trading algorithms in out-of-sample data, identifying any areas of underperformance, and tweaking strategy parameters or logic to address these areas. For instance, a momentum strategy might need its look-back period adjusted, or a mean-reversion strategy might require tweaking of its threshold levels. Regular backtesting and, importantly, forward testing (paper trading) in live market conditions without actual capital commitment can provide valuable insights into how a modified strategy might perform.

Incorporating the latest research findings can give traders an edge. Many traders often overlook the importance of academic journals in finance and econometrics. Journals such as the Journal of Finance or the Journal of Financial Economics often publish papers that can inspire new trading strategies or offer insights into refining existing ones.

However, the rapid evolution of the markets also means that the tools and techniques used to analyze them are constantly changing. This is where the Python trading community plays an indispensable role. The open-source nature of Python has fostered a vast ecosystem of libraries and tools, continuously updated and improved by the community. For traders, this means access to cutting-edge tools, from machine learning libraries like TensorFlow and PyTorch to specialized trading libraries such as Backtrader and Fastquant.

In addition to tooling, the Python trading community is a valuable resource for learning and collaboration. Forums, discussion boards, and social media groups dedicated to Python in finance offer platforms for traders to share insights, discuss challenges, and collaborate on new tools or strategies.

Finally, staying abreast of new library releases and updates is critical. An update might fix a bug, add new features, or improve performance. Regularly checking repositories like GitHub or the Python Package Index (PyPI) can keep traders informed about the latest updates to their favorite libraries.

In conclusion, the landscape of algorithmic trading is one of continuous change and evolution. For those willing to invest the time in continuous learning, adaptability, and community involvement, the rewards can be significant, both in terms of trading performance and personal growth.

Python's dominance in quantitative finance and algorithmic trading isn't accidental. Its versatility, combined with a plethora of specialized libraries, makes it an attractive choice for both hobbyist traders and institutional quant desks. But as with every domain, the landscape of trading is evolving, influenced by technological advancements, new research findings, and changing market dynamics. Here's a glimpse into what the future might hold for Python in the realm of trading.

The current trajectory suggests a growing integration of deep learning and reinforcement learning techniques in trading strategies. Deep learning models, especially recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), show promise in modeling the non-linearities of financial time series data. As computing resources become more affordable and accessible, we can expect Python libraries to emerge, catering specifically to deep learning models for trading.

Reinforcement learning, where trading agents learn by interacting with the market, is another area gaining traction. OpenAI's Gym, a toolkit for developing reinforcement learning algorithms, might pave the way for environments tailored for financial markets, making it easier for traders to train and test RL-based strategies.

Another anticipated trend is the proliferation of decentralized finance (DeFi) platforms and the trading opportunities they present. Python developers are already building tools to interact with blockchain-based protocols and smart contracts. As DeFi platforms mature and become more mainstream, Python's role in developing algorithmic strategies for these platforms will likely expand.

The quantum computing frontier is still in its nascent stages, but its potential implications for trading are profound. Quantum algorithms could revolutionize optimization problems, portfolio management, and even predictive modeling. Python, with its knack for being at the forefront of technological innovation, might soon see libraries and frameworks built for quantum-enhanced trading.

For traders, the future is undeniably exciting, but it also demands preparedness. Continuous learning is crucial. Traders should familiarize themselves with emerging machine learning techniques, stay updated with new Python libraries, and participate in trading competitions to test and refine their strategies against others. Engaging with the Python trading community, attending relevant webinars and workshops, and contributing to open-source projects can also provide insights into where the industry is heading.

Lastly, always remember that while technology and algorithms play a pivotal role, the human element—intuition, skepticism, and ethics—remains invaluable in trading. As we harness Python's potential for future trading innovations, let's do so responsibly and judiciously.

Python has firmly anchored itself in the trading landscape, offering a blend of versatility and robustness that few languages can match. The marriage of Python's powerful libraries with its ease of use has democratized the quantitative trading sphere, enabling individuals and institutions alike to implement sophisticated trading algorithms with relative ease.

As we venture deeper into this fast-paced domain, experimentation and innovation become paramount. Python empowers traders to ideate, test, and deploy strategies in a streamlined manner. However, it's essential to remember that the journey of trading doesn't end at a successful backtest or even a profitable month. The markets evolve, and as traders, we must too. Every misstep, every success, is a lesson waiting to be decoded. By sharing these experiences, be it through open-source contributions, discussions, or community meetups, we collectively elevate the trading community's knowledge base.

For those eager to dive even deeper into the world of Python trading, numerous resources await your exploration. Consider joining forums like QuantConnect or Quantopian's community (though Quantopian's live trading has been discontinued, their community remains a treasure trove of insights). Engage in courses from platforms like Coursera or Udemy that delve into advanced trading topics. And never underestimate the value of Python's own documentation and the vast array of tutorials and guides available online.

In conclusion, as the adage goes: the best way to predict the future is to create it. With Python in your trading toolkit, you're well-equipped to do just that. Embrace the challenges, cherish the learnings, and above all, stay curious. The world of Python-based trading is expansive, and it awaits your unique mark.

💡 Read more:

[1]: High-frequency trading in the foreign exchange market - Bank for International Settlements

[2]: The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution - Gregory Zuckerman

[3]: Alpha Vantage Official Documentation

[4]: WebSocket API for real-time data

[5]: Backtrader Documentation

[6]: pyfolio GitHub

[7]: QuantConnect Documentation

[8]: Fastquant Documentation

[9]: QuantLib Documentation

[10]: Matplotlib Official Documentation

[11]: Pandas Visualization

[12]: Seaborn Official Documentation

[13]: Plotly Official Documentation

[14]: pyfolio GitHub Repository

[15]: Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.

[16]: Hyperparameters and Model Validation in Deep Learning

[17]: "Advances in Financial Machine Learning", Marcos Lopez de Prado, 2018.

[18]: Interactive Brokers TWS API

[19]: ib_insync GitHub Repository

[20]: Binance Websocket API Documentation