Cryptocurrency Algorithmic Trading with Python and Binance

Overview of cryptocurrency trading fundamentals, spot versus derivatives, and Python with Binance API. Download notebooks and data, set up accounts, and practice on Binance spot testnet with paper trading.

Explore the key differences between trading and investing, with a focus on cryptocurrency algorithmic trading, from short-term strategies and leverage to long-term, diversified investing and risk management.

Compare cryptocurrencies with conventional asset classes and fiat money, noting liquidity, transparency, regulation, and stablecoins.

See how stablecoins offer fiat-backed liquidity with 1:1 backing through Tether, enabling faster, cheaper trading between dollars and bitcoin. Convert USD to a stablecoin to move between crypto markets.

Explore the pros and cons of cryptocurrency trading, including high volatility and liquidity, 24/7 markets, and the impact of trading costs, while also examining security risks, scams, and regulatory considerations.

Explore why Binance is the world's largest crypto exchange by volume, offering hundreds of coins and trading pairs, spot and derivatives, plus a powerful API for algorithmic trading.

Explore alternative crypto exchanges beyond Binance, including Bybit, Kraken, and Coinbase, with XRP and ZK libraries to enable cross-exchange trading, liquidity, and testnet paper trading.

Explain spot trading versus derivatives, detailing immediate ownership and delivery in spot markets, and reliance on margin, leverage, and cash settlement in futures, forwards, and contracts for differences.

Compare spot trading and derivatives trading in crypto, illustrating rising and falling prices, long and short positions, and how margin and leverage influence profits, losses, and risk under regulation.

Explore two new role plays—navigating the crypto trading landscape and explaining basics to an intern—and share feedback via the Q&A bot to shape future course content.

Get a quick first look at Binance.com and Binance.US, exploring language and theme settings, markets, spot trading, and the academy, while noting regulatory differences and restricted states.

Register on Binance to create an account with email or mobile, optionally using discount codes to lower trading costs. Complete intermediate verification with country-specific requirements, government ID, and facial recognition.

Discover how to instantly buy your first crypto on Binance by using a credit card or the peer-to-peer exchange, and understand deposits, wallets, and daily limits.

Explore fiat and crypto deposits and withdrawals on Binance, focusing on fees and options like credit card, swift transfer, and advanced cash, while monitoring wallet balances to minimize costs.

Learn how to deposit and withdraw crypto on Binance by selecting the correct network, copying the address, and understanding fees to avoid losses.

Learn to buy bitcoin with euros on a decentralized exchange using a market order for €20, and see how currency pairs, base and quote currencies, and bid-ask spread affect price.

Examine how a first market order converts euros to bitcoin on Binance, with immediate execution, decimal limits, and a 0.1% trading commission under VIP levels and discounts.

The lecture demonstrates selling 0.000055 bitcoin for euro via a market sell order, showing immediate execution, the average price, and net euro after fees.

Market orders execute instantly at the current price, while limit orders set a target price or better, may not fill, and can be canceled for take profit or stop loss.

Execute a take profit order as a limit sell on Binance spot trading after a market buy, targeting a price like 38,000 to secure gains.

Learn to manage risk using stop loss and stop limit orders on the Binance bot platform, setting a trigger price and a buffer limit to protect against rapid declines.

Learn how a decentralized finance exchange matches buyers and sellers with market and limit orders, how limit orders contribute liquidity to order book, and how market makers and takers interact.

Analyze how market orders are filled through the order book, bid and ask prices, and liquidity, and how slippage and the bid-ask spread affect execution.

Explore total trading costs by separating visible Binance commissions from hidden spread and slippage, illustrated with an iceberg analogy and practical buy/sell example.

Explore how liquidity and market depth shape trading costs: high liquidity and large order books yield near-zero bid-ask spreads and low slippage, while low liquidity raises costs.

Explore candlestick charts, price movements, and volume to identify signals, and configure moving averages and key indicators for algorithmic trading with Python via the Binance API.

Install python and set up the anaconda distribution to manage packages and notebooks, update conda, and remove conflicting installations on Windows or Mac before coding cryptocurrency trading algorithms.

install the Python data science ecosystem with Anaconda to manage dependencies and resolve conflicts, and run Jupyter notebooks or other integrated development environments for data science.

Open and explore Jupyter notebooks via the Anaconda Navigator or Jupyter dashboard, then run cells with shift+enter or alt+enter and manage environments with conda list.

Explore how Jupyter notebooks enable interactive Python coding, with editable and command modes, markdown cells, headers, and keyboard shortcuts to streamline learning and debugging.

Receive a Python basics crash course in the appendix, learn about user defined functions, and explore libraries like Nampai Panda's Plot Lab and Seabourne, preparing for Binance API trading.

Develop essential debugging skills through trial and error, learn to fix common errors quickly, and understand why debugging dominates coding time across beginner to advanced Python projects.

Test and sharpen your debugging skills with a hands-on Python exercise that fixes typos, missing keys, type errors, and improper indexing in a Python pandas data task.

Identify three major error sources: code itself, Python installation, and external factors; focus on corrupted code, typos, wrong inputs, and misordered lines, with examples to follow.

Identify and fix the most common Python errors shown, including key errors from missing dictionary keys, typos in variable names, mixed brackets and parentheses, and missing quotes.

Discover how omitting cells and changing sequence affect pandas data frames, triggering key errors, name errors, and index problems, and learn to fix them by restarting the kernel.

This lecture explains index errors in Python using pandas data frames and lists, showing how out-of-range and negative indices trigger errors and how inspecting prior code helps debug.

Master Python indentation by debugging common errors, including unexpected indents and missing required end tokens, with practical examples of dictionaries, iterating keys, and printing key-value pairs.

Demonstrates how misusing Python keywords and built-in function names can break code, such as overwriting the list function with a variable named list, causing callable errors; highlights meaningful variable names.

Explore how type errors occur when operations use unsupported types and how value errors arise from invalid values, with Python examples like concatenation and string-to-int conversion.

Learn to troubleshoot Python coding issues using error messages and Stack Overflow, with practical pandas examples and guidance on positional versus keyword arguments.

Learn to trace complex Python errors using tracebacks, localize issues from recent calls to the offending line, and fix typos or attribute errors in backtesting code.

Identify and fix common Python installation problems in data science workflows, including missing packages, outdated or conflicting installations, and corrupted installations, by managing environments with conda and pip.

Identify external factors that disrupt data retrieval and cryptocurrency trading workflows—internet or server problems, API key authentication issues, and firewall or admin-rights restrictions.

Learn how to handle transcription errors in course code by downloading notebooks, running the provided code in notebooks, and building your own code instead of copying line by line.

Master a debugging flowchart to diagnose and fix cryptocurrency trading code quickly. Apply steps from reading the error to inspecting code, restarting kernels, updating notebooks, and updating installations.

Explore how Python interfaces with Binance through the API to fetch market data, stream live prices, and place trades, using the Veerappa package and an Anaconda setup.

Create a personal Binance API key and secret, paste them into your Python notebook, and configure API restrictions and IP access for safe data read and trading.

Install and use the python-binance wrapper to connect to the Binance API, including updating conda, installing the package, and preparing for API key integration.

Connect to the Binance API with Python and the Python Binance wrapper using your API key and secret, then fetch your account information.

Learn to retrieve market data and trading signals, and fetch general account and system information from the Binance API using the Python wrapper, including balances and status.

Retrieve a daily account snapshot, normalize balances with pandas, and explore exchange data, API limits, and 0.1% trading fees for BTC/EUR pairs.

Retrieve current market data from Binance using Python, including current price, symbol ticker, 24-hour price change, and open, high, low, close, then organize all symbols with pandas dataframes.

Pull historical open, high, low, close and volume data from Binance using the API, convert Unix timestamps to date, and build a time-indexed pandas dataframe for analysis.

Load historical price and volume data from Binance using a gethistory function with symbol, interval, start and end dates, returning dataframe of date, open, high, low, close, and Bitcoin volume.

Download historical market data as CSV from Binance’s site, choose futures or spot for bitcoin-usdt hourly data, unzip the file, and load it into Python with pandas.

Stream real-time market data from the Binance API using threaded WebSocket and Binance socket managers with Python Binance. Learn notebook and script workflows, and streaming of symbol mini ticker data.

Demonstrate running a threaded websocket manager in a Python script by executing the full file path in a terminal or Anaconda prompt, initiating a 20-second live stream.

Define a complex callback to extract the current time and the latest Bitcoin price from the 24-hour mini ticker stream and print them.

Learn to stream real-time candlestick data with the Binance k-line streams, configure one-minute candles, and save open, high, low, close, volume, and completion status into a dataframe.

Learn how to place test orders with the Binance api, including market and limit orders, using your api key and secret, in a safe paper trading spot testnet environment.

Practice algorithmic trading on the Binance spot test network using paper trading, with API and secret key setup, GitHub login, and the official docs and Python wrapper.

Connect to the spot testnet using Binance testnet credentials and set testnet to true, trade with zero commissions, and note withdrawals and deposits are disabled while balances reset monthly.

Explore the spot test net api overview, note the differences from the real api, and learn to connect, check balances, fetch prices, and stream data for paper trading.

Learn to place a market buy order on binance's testnet using python. The lesson covers executing 0.1 bitcoin with usdt, inspecting order details and slippage, and calculating average price.

Discover how to place market sell orders with the binance api, selecting either bitcoin quantity or quote quantity, and review resulting balances and profits.

Place a limit buy order at 50,000 and a limit sell order at 60,000 on the binance spot testnet. Monitor open orders, fetch order details, and cancel orders as needed.

Learn to retrieve all open, filled, cancelled, and historical orders and trades from Binance, convert them to pandas dataframes, and convert Unix timestamps to readable date times for chronological analysis.

Create a simple random trading bot in Python that streams market data and uses the modulo operator to buy 0.1 bitcoin when the price's last digit is zero, then stops.

Explore Python and pandas fundamentals while learning to analyze financial data for trading, covering prices, returns, compounding, risk metrics, and portfolio returns with practical coding exercises.

Install and import the yfinance package to fetch Yahoo Finance data with Python, using the Anaconda prompt and pip install yfinance.

Load historical data from Yahoo Finance with pandas and yfinance by specifying a date range and ticker symbol, then view open, high, low, close, adjusted close, volume, and csv export.

Load and prepare multi-asset data in pandas, convert dates to a datetime index, and swap levels for analysis. Visualize prices for six instruments and compare performance across scales.

Normalize prices to a base value of 100 to compare instruments on a common scale, using relative price changes and log-scale visuals to assess returns and volatility.

Practice loading stock price data from 2015–2020 for a chosen ticker, including Reliance, using close prices, then normalize to a base of one and plot the two-year chart.

Explore price changes and financial returns by converting prices to relative changes with pandas diff and percentage change, and compute returns for Microsoft to compare across instruments.

Learn how to measure reward and risk in investments using arithmetic mean returns and volatility. Use Pandas, NumPy, and Matplotlib to analyze Microsoft price data.

Explore the investment multiple and the compound annual growth rate as intuitive reward metrics to compare returns and measure performance while accounting for the investment period.

Learn how to compute compound returns and the geometric mean return from daily data, and compare with arithmetic return and CAGR.

Explore discrete compounding and how annual, quarterly, and monthly frequencies affect future value and the effective annual rate. Learn that continuous compounding is covered in the next lecture.

Explore continuous compounding, compute future value with e^(rt), and derive the continuously compounded rate from present and future values, including the effective annual rate and lock return.

Compute daily log returns and simple returns for Microsoft, compare their means and volatility differences. Learn how log returns differ and why they matter.

Explore the advantages of log returns over simple returns in asset pricing. Log returns are time additive, avoid the misleading arithmetic mean, and enable end-value calculations by exponentiating summed logs.

Compare simple and log returns to compute investment multiples and normalized prices, and determine the compound annual growth rate using 252 trading days.

Compare six instruments using log returns and mean-variance analysis to reveal that higher risk can bring higher returns, with Bitcoin showing high reward and risk and Boeing underperforming.

Show that financial returns exhibit fat tails and skew beyond mean and variance, using histograms and density plots, and reject the normal distribution hypothesis.

Compute daily log returns, then analyze risk and reward with mean and standard deviation, and annualize using 250 trading days, including compound annual growth rate and continuous compounding notes.

Resample daily prices to weekly, monthly, quarterly, and annual data, then compute the annualized mean returns and the annualized standard deviation; smoothing reduces observed risk, inviting dubious practices.

Compute rolling statistics with a 252-day window to assess rolling mean returns and rolling risk. Explore how regime changes and window length affect analysis and the interpretation of moving averages.

Explore how short selling enables profits from falling prices, illustrated by a stock borrowing and returning shares example. Compare long and short positions in forex and crypto markets.

Explore forex fundamentals, including bid and ask, spreads, pips, base and quote currencies, and how to take long or short euro positions against the US dollar.

Learn about short selling with euro-us dollar pairs, compare long vs short returns, compute simple returns, and apply log returns for short positions.

Show that a long position's simple return differs from the negative of its short, while log returns invert. Normalize prices and compare long versus short euro-dollar trades using Python tools.

Learn how covariance and correlation quantify how assets move together, create a covariance and correlation matrix, visualize with a heatmap, and support portfolio diversification.

Explore building a two-asset portfolio, compute total value and simple returns, compare with log returns, and understand why portfolio returns do not equal the weighted average of log returns.

Explore margin trading with leverage and collateral, and learn why levered returns rely on simple returns, not log returns, including unlevered and levered outcomes and risks.

Analyze how leverage amplifies both gains and losses in margin trading using simple returns, margin calls, and risk management, showing that higher leverage does not guarantee higher profits.

Discover automated cryptocurrency spot trading and building a cloud-based bot with Python and the Binance API, applying enhanced buy-and-hold strategies to the spot market.

Define trading strategies as systematic, rule-based, data-driven approaches for crypto markets, incorporating machine learning and backtested indicators to ensure measurable, reproducible performance from trading data.

Compare passive buy-and-hold versus active strategies, from spot trading to futures, and learn how to progressively scale risk and skills while avoiding gambling-level leverage.

Create, test, and implement your own unique algorithmic trading strategies with flexible coding frameworks. Backtesting and avoiding common mistakes are emphasized, with strategy adaptation for educational purposes, not investment advice.

Load historical bitcoin us dollar data at one-hour intervals from a csv file, containing close prices and volume, for a long-only spot trading strategy.

Visualize the closing prices and trading volume to explore bitcoin trends from 2017 to 2021, then compute log returns, summarize statistics, and inspect hourly distribution with a histogram.

Explore the buy and hold crypto strategy, using normalized prices and lock returns to compare performance and set a base investment of one unit.

Compute hourly and annualized returns and risks for Bitcoin, derive the compound annual growth rate, and assess risk-adjusted performance using return-to-risk ratios to compare buy-and-hold and active strategies.