Algorithmic Trading A-Z with Python, Machine Learning & AWS

Open a long position of 100,000 euros in the euro/usd pair via a contract for differences, then close to realize a profit and compare unrealized and realized profit and loss.

Contrast day trading and long term investing to explain goals and horizons. Day trading seeks intraday profits with leverage, while long term investing builds wealth through diversified stocks and dividends.

Contrast spot trading with derivatives by explaining ownership transfer and delivery, margin and collateral, and how leverage enables bets on rising or falling prices.

Explore how spot trading and derivatives trading differ using bitcoin examples, showing long versus short positions, margin requirements, and how leverage amplifies profits and risks.

Explore day trading across forex, indices, commodities, and stocks using three brokers: Interactive Brokers, OANDA, and FCM, and harness Python APIs for automated trading.

Explore two new role plays that simulate choosing your first algo trading path and explaining basics to a curious intern, as a brand-new feature with optional participation and Q&A feedback.

Explore the OANDA platform from the rest of the world and US perspective, covering forex trading, CFDs risks, platform options, API integration with Python, and demo account guidance.

Learn to create a fully functional demo account for algorithmic trading, including country restrictions, an Australia workaround, and setting up multiple currency accounts with the Oanda hub and API tokens.

Learn how the euro/usd forex pair is quoted, with bid and ask and pips in the spread, and how to buy or sell.

Open a euro long position against the US dollar with a market order, choose units, and monitor and close the trade to realize profit.

Analyze a forex trade by opening a long eur/usd position at 1.1775 and closing at 1.17791. Show the 1.6 pips profit and final balance rise from the 100,000 initial balance.

Analyze trading costs and performance attribution by separating gross profit, cross profit, and net profit after costs, using spreads, bids, asks, and hypothetical mid prices.

Learn how margin and leverage enable trades larger than your account balance, with example of 30:1 leverage and 3.3% margin, plus risk and how to adjust maximum leverage.

Analyze unrealized profit and loss, net asset value, and margin available through a practical margin closeout example, showing how balance, used margin, and price movement trigger automatic position closures.

Learn candlestick charts for day trading, with green and red candles showing open, high, low, and close, plus bars, line, and area chart options and granular timeframes.

Learn how to go short on the euro against the dollar, open a market order, and monitor margin, leverage, and unrealized profit or loss in a forex day-trading setup.

Explore the difference between netting and hedging in forex trading, showing how netting combines trades into one position to cut costs, while hedging maintains two positions with higher spread costs.

Learn how market orders execute immediately at the best price, and how limit and stop orders control entry with price movements and expiry.

Learn to use take-profit and stop-loss orders attached to trades to automatically close positions at target prices, canceling when the position closes, with 100,000 euros and levels 1.19 and 1.17.

Explore a general currency trade by buying Australian dollars with New Zealand dollars, showing trade value, unrealized profit and loss, margin, and currency conversion to the account currency.

Discover interactive brokers (ibkr), a leading, reliable broker with a global footprint across 200+ countries. Explore european market instruments—stocks, options, futures, currencies, CFDs, bonds, funds—and the Python API for trading.

Learn how to create and use a paper trading account on Interactive Brokers, including a free trial with virtual funds, portfolio checks, and switching to a live account when ready.

Install the trader workstation to enable reliable paper trading and API access with Python, by downloading the online version from Interactive Brokers, running the installer, and launching the desktop icon.

Open the trader workstation in paper trading mode, switch to mosaic view, review your paper portfolio and net liquidity, and examine bid and ask prices, candles, and market news.

Place a market order to buy ten Microsoft shares at the current best price, and learn about bid, ask, bid-ask spread, and commissions.

Understand regular trading hours and why liquidity and price efficiency peak during these sessions, with time zones, exchange differences, and the choice to fill outside regular hours in extended periods.

Compare cash and margin accounts, including buying on cash only and borrowing to buy on margin with short selling. Learn risks and suitability for experienced traders.

Explore how trading costs affect stock trading profits, including commissions, hidden costs like bid-ask spreads, with fixed versus tiered fees, US versus international stocks, and order-size implications.

Explore how hidden trading costs arise from bid-ask spreads and liquidity, and calculate total cost per trade using half spread costs and proportional costs on US exchanges.

Compare spot forex trading with CFDs, noting cash trading for currency conversion and CFDs for short selling and leverage, with costs including commissions, spreads, and a $2 minimum per order.

Demonstrates a complete CFD forex trade on Interactive Brokers: going long €10,000, calculating margin and commissions, monitoring unrealized and realized profit and loss, and closing the position.

Analyze a CFD euro/usd trade: a long 10,000-unit position from 1.06295 to 1.06325, where spread and commissions turn $3.21 pre-cost profit into a $1 loss given leverage and €333 margin.

Explore the website overview, including markets like forex, shares, commodities, crypto, and baskets, plus platform options and country availability. Understand risk disclosures, education, and a demo for Python-based algorithmic trading.

Open a demo trading account to practice with virtual funds in a risk-free environment. Register with name, email, and country, then log in and trade forex, indices, and commodities.

Place a market order to buy EUR/USD, choose 1000 units, view the price and unrealized profit and loss, and close to realize a small profit.

Analyze a euro/usd trade: long 100,000 units opened at 1.19298 and closed at 1.19305, yielding about $7 profit and €5.87 after conversion, with roughly $11–$12 costs.

Explore price charts for day trading, focusing on candlestick charts with open, high, low, close, and adjustable granularity from minutes to weeks, including line charts and table view.

Netting minimizes trading costs compared with hedging, and a practice account cannot become a netting account; closing a position instead of reversing avoids duplicate fees in a 100000-euro FXE example.

Explore how to place market, limit, stop and entry orders, set take-profit and stop-loss levels, and manage open positions in the euro/US dollar market.

Learn how to install Python and required packages for algorithmic trading, use Anaconda to manage versions, and run notebooks to build Python-based trading algorithms.

Install the Anaconda distribution to set up Python and a complete data science environment with pre-installed packages, supporting Jupyter notebooks and popular IDEs, with platform-specific installation guidance.

Open the Anaconda Navigator, launch Jupyter notebooks, and run Python code. Manage base environments, view installed packages like NumPy and pandas, and use Shift-Enter or Alt-Enter to run cells.

Learn how to use Jupyter notebooks as an interactive Python coding environment, switching between edit and command modes, using Markdown for headers, and running cells with keyboard shortcuts.

Review the installed Anakonda and familiar notebooks, then follow the Python crash course and appendices for Python basics, finance basics, and later panel and matplotlib skills for trading.

Master debugging essentials and learn to fix errors quickly through trial and error, using common sense to accelerate coding in Python, often fixing issues in under a minute.

Test and strengthen debugging skills by fixing Python errors in a hands-on quiz. Resolve typos, type mismatches, and indexing mistakes to build data analysis with pandas series.

Identify three major error categories in Python programs—code problems, installation issues, and external factors—with code problems being most likely, and illustrate examples like typos and incorrect inputs.

Identify and fix the most common Python errors in algorithmic trading code, including missing dictionary keys, typos, syntax mistakes, and misused method calls, to keep algorithms running smoothly.

Learn how omitting cells and changing cell order disrupts pandas data frames, index handling, and variable state, and practice debugging tips like restarting the kernel to restore correct outputs.

Examine index errors in pandas data frames and lists using position-based indexing and negative indexing, and learn to debug by inspecting earlier steps to find the root cause.

Diagnose and fix Python indentation errors by recognizing unexpected indents and missing required indents, using dictionaries and for loops to print key-value pairs with correct indentation.

Avoid misusing python keywords and built-in function names. Use meaningful variable names to prevent overwriting list or other callables.

Learn to distinguish TypeError from ValueError with practical Python examples like mixing types, converting strings to numbers, removing missing list items, and negative square roots.

Develop troubleshooting skills by using Stack Overflow and Google to resolve Python errors, including understanding positional and keyword arguments with pandas examples.

Learn how Python tracebacks help you quickly locate and fix complex errors in backtesting code, identify typos, and pinpoint failing lines from top to bottom.

Identify missing packages, corrupted installations, and conflicting python setups, then fix by installing a full Anaconda distribution and using conda and pip to install pandas data reader and related libraries.

address external factors disrupting data pipelines, such as weak internet, server errors, and authentication or eligibility issues, and offer debugging tips plus troubleshooting resources for firewall or admin rights constraints.

Download all notebooks, exercises and coding lines, and avoid transcribing code from videos; instead run the notebooks and learn by coding to create your own projects.

Master a debugging flowchart to diagnose and fix errors quickly in trading code by reading error details, tracing the code, restarting the kernel, and leveraging online resources.

Learn to trade with Python using the Wander and FCM APIs, connecting via APIs to trade from Python, with options to watch Wander-only or FCM-only parts.

Install the OANDA rest v20 API with the Tepco x wrapper, open a v20 trading account, and run pip and conda commands to set up your environment.

Prepare to use the oanda render api by downloading the render first steps resources, unzipping them, and configuring wanda.cfg with your account id and a new access token, then save.

Connect to render API and Windows server with Python using Tpco wrapper, authenticate via a config file, and use API to pull historical and real-time data and view account info.

Load historical price data to develop and backtest trading strategies using pandas, retrieving euro–US dollar bid prices with daily granularity and OHLC candlesticks for in-sample and out-of-sample testing.

Load intraday price data from OANDA with hourly, minutely, and five-second granularity. Use gas prices or the S&P 500 as examples and note UTC to New York time boundaries.

stream real-time price data from Oanda to feed a Python trading algorithm, using five-second candlesticks, bid and ask prices, and the instrument parameter such as euro/usd with configurable tick limits.

Master how to place orders and execute trades with the OANDA API, by creating euro and US dollar orders, setting units and stop loss distance, and reviewing transactions.

Learn to automate trading with Python by installing IB Async, migrating from IB In Sync, configuring the trader workstation API settings, and importing IB Async for subsequent steps.

Learn how to connect to Interactive Brokers via the API using a wrapper package, log into the Trader Workstation, create an IB object, connect, view current positions, and disconnect.

Learn how to create contracts in the Interactive Brokers API, obtain contract IDs, and use ticker, exchange, and currency to avoid ambiguity with forex and Apple stock via smart routing.

Learn how to fetch market data in Interactive Brokers by creating contracts for currencies and stocks, noting currencies always provide live data.

Stream market data for multiple tickers in parallel, printing real-time prices every second for euro/usd, Apple stock, and a third instrument; apply these prices to build indicators and buy/sell signals.

Learn to locate the correct IBKR contract by handling unambiguous, unknown, and multiple results, then retrieve contract details and visualize contracts with a pandas dataframe.

Explore forex contracts and their derivative CFD counterparts, noting margin trading with leverage, short selling, and regional CFD restrictions. Requires a forex market data contract and a CFD order contract.

Place market buy and sell orders via the Interactive Brokers API, creating contracts. Monitor trade status, use ibkr sleep for fills, and review average fill price.

Demonstrate a CFD trade on the euro/usd: connect, create a €10,000 CFD contract, and place a buy to open a long. Then close with a market sell and outline outcomes.

Analyze a CFD trade in IBKR by parsing execution data, building a dataframe, and calculating trade value, commissions, and realized profit and loss in USD.

Pull portfolio positions and account values from the Interactive Brokers API into Python, convert to data frames, and extract symbol and contract id alongside euro and usd cash balances.

Demonstrates loading historical bars from the Interactive Brokers API for forex and stocks, contrasts data subscriptions with Yahoo Finance, and explains bar sizes and duration settings.

Learn how the FXCM API wrapper provides programmatic access to the trading engine, enabling historical and real-time data in Python and easy trade placement from your app.

Download the FXCM CME First Steps resources, extract them, and review the config file and notebook to set up authentication with your personal access token.

Connect to the fxcm api and cms server using python and a wrapper package, load a config file, access accounts, instruments, historical and real-time data, and place orders.

Load historical euro/usd data with the get candles method to build and backtest and forward test trading strategies using hourly OHLCV candles, bid/ask, and volume with configurable granularity.

Learn how to load EUR/USD historical price data with FXCM, using a start and end date, candles, ask prices, and granularity from minutes to months, with a 10000 row limit.

Learn to stream real-time market data from the CME API, subscribe to euro/usd, and feed python trading algorithms with live last prices and ticks.

Learn to place market by order and market sell orders for euro/usd, execute trades via the s.m API, and close positions for a daily summary.

Master the mechanics of day trading and forex trading while establishing python infrastructure, then move toward defining trading strategies and building algorithms with python and pandas.

Develop pandas-based time series and financial data skills for part two, covering time series basics, time zones, appendix crash courses, plus object oriented programming to build a financial instrument class.