Learn Algorithmic Trading in One Day

Learn how to set up your first Jupyter Notebook for algorithmic trading, launch Anaconda Navigator, create and organize a project folder, write and execute Python cells, and inspect data types.

Learn to work with strings in Python, including quoting with single or double marks, printing, and concatenating strings with variables and numbers, using type checks and conversions.

Explore NumPy basics in this second part, generating linearly spaced values between two bounds with start, end, and count; compute mean, standard deviation, and variance, noting sqrt of the variance.

Explore NumPy Part 3 by building and reshaping arrays into 3D structures, locating and filtering numbers with boolean indexing, and using seed to ensure randomization for testing in algorithmic trading.

Explore pandas data manipulation by selecting specific columns or cities and setting a column as an index. Add and drop columns, and perform in-place changes with axis settings.

Learn to plot financial growth with scatter plots and candlesticks, update layouts and axes, and enable time-filtered Apple stock charts using sliders.

Get live market data from Yahoo Finance API, fetch minute-to-minute prices, and visualize candlestick charts for multiple symbols, including Amazon, Microsoft, and Uber.

Acquire live crypto data from the Binance API by securing an API key and secret, then fetch, format, and visualize historical data for moving average analysis.

Identify the main market players and the major exchanges—NYSE, Nasdaq, Japan Exchange Group, and London and Paris—to see how governments, companies, banks, institutional funds, and individuals provide capital.

Learn how exchange traded funds, mutual funds, and exchange traded notes shape capital markets, detailing structures, costs, liquidity, counterparty risk, and investment strategies.

Examine stock, bond, forex, commodity, and cryptocurrency markets to build a diversified portfolio. Learn how the S&P 500 reflects major U.S. market capitalization and informs diversification strategies across assets.

Explore moving averages, including sma and ema, and how short term and long term periods filter market noise, generate crossovers, and signal trends with confirmations from macd and other indicators.

Learn to implement simple and exponential moving averages in Python using pandas. Visualize these indicators on Binance market data and understand how weighting recent prices affects signals.

Explore the relative strength index (RSI) in a Python app, calculating RSI manually and with a library, using live bitcoin data to generate oversold/overbought entry and exit signals.

Explore Bollinger bands built from moving averages and standard deviation to identify support and resistance, assess volatility, and spot buy or sell signals and potential market corrections.

Compare Twitter and Snapchat stock performance during the COVID-19 crisis by automating bulk data import, visualizing market data, and developing backtesting to measure returns.

Learn to compute daily returns from closing prices, derive cumulative returns, and plot price and volume to visually assess correlations and volatility in stocks.

Define middle, upper, and lower bands using a 20-day window and ±1.96 standard deviation, implement the Python trading logic, and generate Netflix buy/sell signals on a plotted map.

Deploy your algorithm by downloading market data and applying moving averages, including a 10-period average, to replicate market evolution and identify swing points.

Explore how to download S&P 500 market data and deploy a moving average to create support levels, using a Python package and a 50-period setting with visualization.

Identify recurring market patterns and support levels, analyze why the October–November 2020 period behaved differently, and practice predicting future moves through pattern recognition.

Analyze market data by visualizing candlestick patterns and moving averages to identify support and resistance levels, and uncover profitable price patterns for quick, rule-based trading decisions.