Algorithmic Trading with Python - Alpaca & Polygon

Download and install the Anaconda environment across Windows, Mac, and Linux, selecting the 64-bit option on Windows, and complete the installation as shown.

Open Anaconda Navigator and launch Jupyter Notebook to start coding in Python. Create a new notebook, navigate files, run and edit cells with shortcuts like Shift+Enter.

Explore Python basics in a Jupyter notebook by reviewing core data types: strings, lists, integers, floats, dictionaries, booleans, and None, along with variables and type checks.

Demonstrate Python operators using a, b, and c to show addition, subtraction, multiplication, division, modulus, string concatenation, and boolean comparisons, preparing for loops.

Learn to use for loops to iterate strings and lists, print items, and update a running value, then explore while loops and indentation that stop when the condition is met.

Define and call functions using def, name parameters, and return results; illustrate with a tax payment function that computes income times tax percent and returns total paid.

Learn to install pandas via pip, import the library as pd, and build data frames from API data for simple analysis, using head and tail to inspect data.

Sign up for alpaca and create an individual trading API account by completing the registration, verifying your email, and logging in to preview the dashboard in the next video.

Explore the Alpaca dashboard in paper trading, reset your balance, place a market order for Apple, and track cash, buying power, and positions.

Explore the broker API documentation for building trading apps and brokerage services for end users. Note that the course does not review this section in depth.

Discover Alpaca's trading API for stocks and crypto with paper trading, and pull assets, orders, and positions using Python, including fractional trading, authentication, and market data integration.

Explore Alpaca's market data API to access real-time and historical data for equities, options, and crypto, including pricing tiers, authentication, and key limits for course-based algorithmic trading.

Learn to authenticate to the Alpaca trading API by generating and securely storing key and secret, building a requests-based get call with proper headers, and verifying a 200 response.

Access the accounts endpoint in the trading API with Python, using auto-generated code to set headers from your key and secret, and pull your buying power and cash.

Retrieve dividend announcements related to corporate actions via the trading API by applying search criteria and required parameters, then filter with a 90-day date range.

Fetch all open positions, verify holdings (Apple and Microsoft) in the dashboard, and close all or specific positions by asset ID, with optional cancel of open orders and JSON verification.

Retrieve portfolio history through a get request, returning time-series equity and profit or loss, convert the data into a pandas dataframe, and analyze daily account value.

retrieve and update Alpaca account configurations using api keys, explore fractional trading, and practice updating settings in a notebook.

Learn to fetch and filter your Alpaca account activities with Python, using get requests and query params to view all or only fills via activity type filtering in Jupyter.

Explore the calendar API to fetch market days from 1970–2029, with open 9:30 a.m. and close 4 p.m., including early closures on holidays, via Python in Jupyter.

fetch the current market time stamp and open status with the clock API, and show the next open and close times by parsing the json response.

Get a high-level overview of crypto funding, including wallet funding and crypto symbols like btc usd and eth usd, while the course does not dive into crypto implementation.

Explore the Alpaca Market Data API’s historical auctions to fetch stock prices by symbol and date, authenticate with keys, and parse JSON in a Python notebook.

Learn to pull historical bar data for stocks using JSON requests, set symbols and timeframes, and organize results into pandas dataframes with open, high, low, close, volume, and time fields.

Use the next page token to paginate the API requests and loop through pages to build a complete data set with Pandas by concatenating each page until no token remains.

Fetch the latest bars for ticker symbols using the latest bars endpoint, returning open, high, low, close, and volume with yesterday's end-of-day values when the market is closed.

Explore historical quotes for Apple via the historical quotes API, showing symbol-ordered data and condition codes; learn that R means regular market maker open and how to paginate results.

Map stock exchange codes to exchange names by loading a JSON payload in a Jupyter notebook, showing all exchanges and their codes (IEX, V) with API keys.

Fetch the latest bid and ask quotes for tickers via the multi quotes endpoint, using Apple as a live example in UTC time.

Fetch snapshots for multiple tickers via the snapshot endpoint to retrieve latest trade, latest quote, minute bar, and daily bars in one call for Apple on the IEX feed.

Explore historical and latest stock trades with the Alpaca market data API, pulling multi-symbol data via IEX SIP and pagination, using Apple and Meta examples.

Fetch the ten latest stock and crypto news articles in a jupyter notebook, with configurable limit, date range, and sorting. Retrieve article headlines and links for deeper insights.

Explore corporate actions in the market data API by querying symbol-specific actions over a date range, including forward splits, cash dividends, and stock splits, as shown with Apple and Tesla.

Pull an option chain with the Alpaca market data api using Apple as the example. Adjust parameters and present results in a dataframe showing ask and bid prices.

Evaluate polygon.io data options alongside alpaca data, decide whether to use polygon, sign up at polygon to access free data, then continue to the next lecture.

Explore Polygon pricing for stock data, including starter and basic plans, data limits, and separate pricing for stocks, options, indices, and currencies to guide plan selection.

Explore Polygon stocks documentation to access rest endpoints for latest US stock market data, company financials, holidays, and corporate actions, using the Python client library with a stocks focus.

Use polygon.io aggregates to pull stock data over a date range, then build a pandas data frame from the api response with a ticker column and epoch conversion to dates.

Query grouped daily bars to retrieve open, high, low, and close for all stock tickers by date, transform the response into a data frame, and clean ticker data for analysis.

Fetch daily open, high, low, close, and volume, including after hours and pre-market data, for a symbol on a chosen date using polygon in a Jupyter notebook with interactive calendar.

Retrieve the previous close data for a stock ticker using the Polygon API, including open, high, low, close, volume, and time, and practice in a Jupyter notebook.

Learn to access trades, last trade, quotes, and last quote via market data endpoints, and consider upgrading to a developer account for ticker symbol time range data.

Learn to pull data for SMA, EMA, MACD, and RSI from polygon market data endpoints in a Jupyter notebook, convert to a pandas data frame, and format timestamps to dates.

Explore reference data endpoints by fetching exchanges and tickers with Python, using the exchange parameter to filter stocks, and converting results into a clean data frame.

Pull ticker details for Meta via the V3 endpoint in a Jupyter notebook, using the default most recent date, and inspect shares outstanding and market cap.

Explore the ticker events endpoint in Polygon using Python in a Jupyter notebook, querying entities by ticker, q, or composite fi, and review the ticker change from Facebook to Meta.

Explore polygon.io ticker types and asset classes, including common stock, preferred stock, warrants, rights, and corporate bonds, using a Python notebook and data frames for analysis.

Fetch market holidays and current market status using Polygon APIs, convert results to a pandas data frame, and interpret open hours, holidays, and early closes.

Fetch historical stock splits with alpaca and polygon by symbol, date, and split ratio. Apple shows three splits since 2005: 2-to-1 in 2005, 7-to-1 in 2014, 4-to-1 in 2020.

Fetch historical cash dividends for Boeing using a Jupyter notebook, including ticker, declaration date, ex-dividend date, record date, pay date, frequency, and amount, displayed as a data frame.

Fetch historical stock financials from XBRL via an experimental API, pull the ten most recent SEC filings for a ticker, and inspect cash flow, income statement, and balance sheet data.

Explore the Polygon.io conditions list by setting up a Jupyter notebook, querying the endpoint, and converting results into a pandas data frame to view condition mappings, including update rules.

Learn to use polygon's related companies endpoint to fetch tickers related to a query ticker, such as Apple, and present the results in a data frame.