Machine Learning for Quant Finance and Algorithmic Trading

Explore Pandas basics, including data frames, series, and data cleaning, and learn to load, analyze, and visualize data for finance and ML workflows.

Learn to create and use pandas series, a one-dimensional data structure. Treat it like a dataframe column, with indexing, built from lists or dictionaries.

Explore how a pandas data frame acts as a two-dimensional structure with rows and columns, and learn to create it from a Python dictionary and access data with df.loc.

Learn how to read csv files into a pandas data frame with read_csv and print the result, using files like data.csv, schedule.csv, and airport.csv in the same directory.

Learn to analyze pandas data frames by using head and tail to view data, and info, describe, and correlation to summarize data and relationships, with practical notebook examples.

Learn NumPy, the Python array library, how to install via pip, import as np, and use ndarray for faster data processing in ML and data science.

Learn numpy array indexing to access 1d and 2d elements using zero-based indices, including simple operations like adding a[2] and a[0], with practical code examples.

Learn to iterate NumPy arrays with Python for loops, from 1D to 2D, and print or access elements. Use nested loops to access each scalar element in a 2D array.

Learn array slicing in Python and NumPy, including how start, end, and step select elements in 1d and 2d arrays, with zero-based indexing and default end behavior.

Search numpy arrays with np.where to locate matching indexes and even elements, then sort numeric and alphabetic arrays in ascending or descending order.

Install and import matplotlib, use plt to plot a simple line chart with numpy, and explore common plots like line, bar, scatter, and pie charts.

Master matplotlib plotting basics for data analysis, including line charts, bar charts, scatter plots, pie charts, and histograms, using numpy arrays and pyplot functions like plt.plot and plt.hist.

Learn seaborn, a Python plotting library, install with pip, and use inbuilt datasets like tips and Titanic to generate box plots, dist plots, and ridge plots.

Learn to identify and handle missing values in data using pandas and imputation techniques. Replace numeric missing values with mean, categorical with mode, and use simple imputer for missing categories.

Use feature scaling in data preprocessing by normalizing or standardizing features with min max or standard scalers to align ranges, such as age and salary, for algorithm performance.

Explore feature encoding techniques for converting categorical variables into numeric representations. Learn nominal and ordinal encodings, including one-hot, dummy, mean, label, and target-guided ordinal encoding, with practical pandas demonstrations.

Master supervised machine learning with labeled data, features, and labels to train models that predict outcomes, evaluate with metrics like accuracy and rmse, and apply to quant finance.

Master unsupervised machine learning by learning from unlabeled data to uncover hidden patterns using clustering and dimensionality reduction, with applications in segmentation and portfolio optimization for quant finance.

Explore the machine learning lifecycle from data gathering to deployment, and train models using CSV files and Yahoo Finance for quant finance and algorithmic trading.

Learn how to perform a train test split in supervised machine learning, using 75/25 training and testing partitions, with random sampling and scikit-learn's train_test_split to validate models on unseen data.

Learn how to evaluate machine learning models on unseen data using metrics like confusion matrix, accuracy, precision, recall, and mean squared error to improve generalization.

Understand dimensionality as the number of features in a dataset, curse of dimensionality, and how dimensionality reduction, including feature selection and extraction, PCA and t-SNE, improves performance, visualization, and computation.

Learn how regression analysis models the relationship between dependent and independent variables to predict continuous values, covering linear regression, SVM, decision trees, random forests, outliers, multicollinearity, and overfitting.

Demonstrates regression as a method to predict a dependent variable from independent features. Shows equation y = a0 + a1x, builds the model with sklearn, and visualizes salary versus years of experience.

Explore logistic regression, a supervised classification algorithm that maps inputs to probabilities with a sigmoid function, using a 0.5 threshold to predict yes/no and evaluate with a confusion matrix.

Understand the k-nearest neighbors algorithm as a simple, supervised, non-parametric classifier. See how it uses Euclidean distance and stores data for practical Jupyter notebook examples.

Explore how a support vector machine builds the optimal hyperplane with maximum margin using support vectors, for linear and non-linear classification (and regression), with an 80/20 train-test split.

Discover how a decision tree uses a root node, decision nodes, and leaf nodes for classification and regression via the CART algorithm with entropy. Explore a sklearn implementation predicting purchases.

Explore how random forest, a supervised learning ensemble, aggregates multiple decision trees on data subsets to improve classification or regression and reduce overfitting.

Explore k-means clustering, an unsupervised, centroid-based algorithm that assigns data to k clusters by nearest centroids and updates centers, using the elbow method to choose k.

Explain hyperparameter optimization with grid search cv, clarify parameters versus hyperparameters, and demonstrate tuning C, gamma, and kernel for an SVM on breast cancer data.

Explore the machine learning pipeline concept and its practical implementation across data ingestion, cleaning, preprocessing, feature transformation, modeling, and deployment. See how modular, independent steps automate the full ml workflow.

Build a machine learning app to predict medical insurance costs from age, sex, BMI, smoker, and region using linear regression; encode categories, perform train-test split, and assess with r2.

Develop a diabetes prediction model from a dataset using features like glucose, blood pressure, BMI, and insulin, standardized and evaluated with a linear SVM.

Explore activation functions that bound outputs and enable nonlinear, differentiable backpropagation, covering sigmoid, softmax, tanh, ReLU and its leaky or randomized variants for binary, multi-class tasks and CNNs.

Discover how optimizers adjust weights and learning rate to minimize loss, and compare gradient descent, stochastic gradient descent, mini-batch gradient descent, and Adam.

Explore convolutional neural networks (CNNs) for visual data, learning filters and reducing parameters through pooling, with practical TensorFlow and Keras demonstrations of image preprocessing, convolution, stride, and pooling.

Describe how recurrent neural networks use feedback from previous states across time steps, train with backpropagation, and employ LSTM and gated recurrent unit variants for time-series data.

Explore the major financial markets—stock, commodities, forex, bonds, and crypto—and learn how news and demand drive stock price movements, illustrated with Infosys case studies and price discovery.

Explore the foreign exchange market, a 24/7 over-the-counter arena trading currency pairs and their base and quote currencies, with an overview of commodities, bonds, and cryptocurrencies.

Learn how the time value of money drives present and future value calculations, incorporating interest, compounding, inflation, and opportunity cost to inform investment decisions.

Explore fundamental, technical, and quantitative analyses for financial markets. Learn intrinsic value, valuation metrics, price patterns, and data-driven modeling to forecast trends and inform trading decisions.

Learn how the capital asset pricing model linearly links required return to risk via beta, the risk-free rate, and the equity risk premium to price risky securities.

Learn modern portfolio theory, a diversification-based framework that maximizes expected return for a given portfolio risk by evaluating variance, correlations, and risk-adjusted returns across assets like government bonds.

Understand how correlation measures how two securities move together, calculated as a coefficient between minus one and plus one. Apply this to portfolio diversification, risk management, and benchmarking against indices.

Create a stock correlation matrix in Python, fetch prices with Yahoo data reader, compute Pearson correlations, and visualize with Seaborn heat map. Explore diversification and hedging via non-correlated stocks.

Explore the Kelly criterion, a money-management formula that calculates the optimal allocation (K percentage) from win probability and win-loss ratio to optimize bets, diversification, and portfolios, with backtesting guidance.

The Sharpe ratio compares an investment's excess return over the risk-free rate to its volatility, calculated as (r_p - r_f) / sigma_p, to measure risk-adjusted performance.

Pair trading pairs a long and a short in two highly correlated stocks to seek market-neutral profits. Profits arise when the pair converges back to historical correlation.

Defines arbitrage as the simultaneous purchase and sale of the same asset across markets to profit from price differences, exploiting market inefficiencies for risk-free gains in stocks, commodities, and currencies.

Understand how derivatives derive value from underlying assets and trade on exchanges or over the counter. Learn about futures, forwards, options, and swaps for hedging, speculation, and leverage.

Learn how futures contracts, as standardized derivatives, let you buy or sell an underlying asset at a predetermined price on a future date, enabling hedging, speculation, and leverage.

Explore how options grant the right, not the obligation, to buy or sell an underlying asset at a strike price, using call and put contracts, american versus european, and greeks.

Explore the Black-Scholes model, a differential equation for pricing European options using five inputs: stock price, strike price, time to expiry, risk-free rate, and volatility. Understand its assumptions and limitations.

Understand that price action encodes all information and use charts on tradingview to predict market trends. Learn candlesticks, open/high/low/close data, and volume to interpret bullish and bearish days.

learn to plot a candlestick chart in python using mplfinance and the candlestick_ohlc function, visualizing open, high, low, close and time with green bullish and red bearish candles.

Master the core concepts of support and resistance as drivers of price movement in technical analysis, based on supply and demand, and identify levels with trend lines and moving averages.

Explore moving averages in finance, including simple moving average (SMA) and exponential moving average (EMA), their calculations, smoothing effects, and how they reflect recent price changes.

Learn how to compute a 30-day simple moving average using pandas rolling and plot it against stock closing prices to analyze trends.

Learn to compute the exponential moving average of stock prices in Python using pandas, with a 30-day span, producing a weighted, more responsive trend line than a simple moving average.

Explore chart patterns in technical analysis, including pennants, flags, wedges, triangles, cup and handle, head and shoulders, and double tops and bottoms, to anticipate price movements for quant finance.

Explore the Dow theory's role in technical analysis, including market discounts, three kinds of market trends, volume confirmation, cross-index validation, and accumulation, markup, and distribution phases.

Analyze the RSI, a momentum oscillator, to detect overbought and oversold conditions, signal potential trend reversals, and apply a 14-period calculation with divergence insights.

Fetch and parse ohlc stock data in Python using yfinance, access company info from the info dictionary, and pull historical open, high, low, close, and volume.

Predict Apple stock closing price with linear regression using three- and nine-day moving averages. Utilize 2008–2020 data, Yahoo Finance, dropna, train-test split, fit the model, and assess with R2.

Learn to predict gold prices with machine learning using a random forest regression model, based on SPX, gold price data, US oil, silver, and Euro USD, evaluated via R2 score.

Predict Tesla stock prices with logistic regression, SVM, and XGBoost using day, month, and year date features. Assess ROC AUC on a 90/10 split and evaluate with a confusion matrix.

Analyze Amazon stock with pandas and matplotlib, develop a moving average crossover trading strategy, and backtest performance to generate buy and sell signals.

Check if Streamlit is installed via the command prompt and note the version; install it with pip install Streamlit if missing, enabling Streamlit for quant finance workflows.

Learn to display text in a streamlit web app using st.title, st.header, st.subheader, st.markdown, st.code, st.text, and st.write, with practical examples and variable outputs.

display data frames in Streamlit with st.data_frame, using a Pandas data frame or NumPy array as the source, and explore an interactive table with column sort.

Explore static tables in Streamlit by using st.table to display a pandas data frame as a non-interactive tabular output.

Learn to display JSON data in a Streamlit app using st.json, passing a serializable object and an optional expanded flag, and preview a neat, pretty JSON output.

Learn to plot a line chart in Streamlit with st.line_chart, using a pandas data frame built from NumPy random data and configuring x and y axes.

Learn to create an area chart in Streamlit with the area_chart function. Generate a Pandas data frame with NumPy-generated data, and map x, y, z for the chart.

Learn to plot a bar chart in Streamlit using a pandas data frame with random values, specifying data source, x axis and y axis, width, and height.

Plot matplotlib.pyplot plots directly in a Streamlit app using the st.pyplot function, demonstrated by a numpy-generated normal histogram rendered in the Streamlit interface.

Learn how to implement a button widget in Streamlit using st.button with a label, optional markdown, on-click callback, and an optional key, then display messages based on the button state.

Explore how to create and use radio buttons in Streamlit with st.radio, including labeling, option lists, and simple if-else logic to respond to user selections in a mini app.

Learn to create a text input in Streamlit using st.text_input, set a label and default value, store the input in title, and display the current title on the local host.

Learn to use Streamlit's number input with st.number_input, bind the value to a num variable, and display it with st.write for dynamic, validated numeric input.

Learn to add a download button in a streamlit app to enable downloading text, CSV data, and images using st.download_button, with utf-8 CSV conversion.

Explore how to implement a Streamlit checkbox, including label, value, key, and onchange arguments, and see a live demo that displays a message when selected.

Learn how to implement a select box in Streamlit to capture user contact preference, with options email, home phone, and mobile phone, showing the selected value.

Import streamlit as st and use date_input to capture a user’s birth date, store it in d, convert with datetime.date, and display a calendar widget for date entry.

Explore the slider input widget in Streamlit, including an age slider, a range slider, a range time slider, and a date time slider for interactive web apps.

Learn to display images in a Streamlit web app by importing PIL, opening test.jpg from the local folder, and rendering it with an image function plus a caption.

Learn to work with video files in Streamlit by uploading a readymade video, reading it as bytes, and displaying it with st.video in a local host app.

Explore how to build a Streamlit sidebar with st.sidebar, add a select box and a radio button, and run the app locally.

Explore columns in Streamlit by creating a three-column layout that places headers and text side by side, using the with notation to populate each column’s container.

Explore expanders in Streamlit by building a bar chart and wrapping descriptive text inside an expander, using st.expander, a width option, and st.write to reveal content.

Build a Streamlit web app to predict a stock market index using economic growth rate and unemployment rate with a linear regression model, including data framing and visualizations.