Automated Cryptocurrency Portfolio Investing with Python A-Z

Explore the basics and prerequisites for automated cryptocurrency portfolio investing with Python. Learn trading fundamentals, set up Binance and FD accounts, install Python, and analyze data with pandas and Coingecko.

Compare trading and investing in crypto, highlighting short-term, high-leverage strategies versus long-term, diversified portfolio investing, with data-driven decisions, backtesting, and risk management.

Compare cryptocurrencies to conventional and alternative asset classes by examining liquidity, regulation, and transparency. Explore how fiat money, representative money, and virtual money relate to crypto use and future status.

Examine stablecoins as hybrid fiat-backed crypto assets that peg to the dollar, including tether; assess trust and audit concerns, and how traders convert USD to stablecoins for cheaper crypto trades.

Assess the pros and cons of cryptocurrency trading, including high volatility, liquidity, costs, decentralization, and risks like scams and manipulation, in a 24/7 market.

Explore the cryptocurrency market overview, including coins, exchanges, and data aggregators, and learn how Coingecko's free API enables data-driven portfolio investing with Python.

Explore Coingecko as a data aggregator, learn how market cap, trading volume, and dominance are calculated, and preview integrating Coingecko data with Python for portfolio management.

Explore how cryptocurrency prices, volumes, and charts are displayed and calculated on Coingecko, with examples from Bitcoin, including price aggregation, 24-hour and 7-day ranges, and historical data.

Understand how market capitalization for Bitcoin is price times circulating supply, compare it to stock markets, and examine total supply, max supply, and fully diluted valuation.

Explore centralized, decentralized, and derivatives exchanges, focusing on spot markets on centralized platforms like Binance and FTX. Review trading volume, coins, trading pairs, and exchange fees.

Explore Binance, the world's largest cryptocurrency exchange by trading volume, offering spot and derivatives, more than 500 coins, more than 1200 trading pairs, and a powerful API for algorithmic trading.

Explore Binance dot com and Binance dot us at a glance, comparing interfaces, markets, identity verification, and regulatory changes, with country restrictions and regional differences.

Guides you through creating a Binance account with email or mobile registration and applying a discount code. Explains intermediate identity verification with government ID and address proof to access services.

Learn how to instantly buy crypto using a credit card, choose your native currency, and explore direct deposits, wallet overview, and peer-to-peer options on Binance.

Discover how to deposit fiat into Binance and withdraw funds with low costs. Explore euro and USD options, credit card fees, SEPA limits, and wallet balance tracking.

Learn how to deposit and withdraw cryptocurrencies to and from Binance. Select the correct network and ensure the deposit and withdrawal networks match to avoid asset loss and withdrawal fees.

Learn to execute a first spot trade by buying bitcoin with euros on the decentralized Binance exchange, using market orders and currency pairs like bitcoin/eur, while reviewing wallet balances.

Explore how a euro to bitcoin market buy executes instantly, review of order history, and how trading commissions, VIP levels, and Binance coin discounts impact net crypto purchases.

Sell 0.00055 bitcoin for euros with a market order on the btc/eur pair, executed immediately, netting about €19.87 after fees. Review the order history for price and fee details.

Explore market orders that execute instantly and limit orders that buy or sell at a limit price, with examples on open, canceled, take profit, and stop loss orders.

Explains how to set a take profit order as a limit sell on the binance bot trading platform, using a bitcoin example from 36,000 to 38,000 and separate order creation.

Learn how stop loss and stop limit orders protect crypto investments on Binance, using trigger prices like 35,000 and limit prices such as 34,900 to automate risk control.

The lecture shows how the decentralized Binance exchange uses an order book to match buyers and sellers, explains market orders versus limit orders, and highlights liquidity's role.

Explore how market orders interact with the order book to reveal bid-ask spread and slippage, and learn how liquidity affects price and execution.

Learn how bid-ask spread and slippage create hidden trading costs beyond Binance's 0.1% commission, illustrated by an iceberg analogy and total trading cost concepts.

Explore how to trade across exchanges beyond Binance using a Python library, with Binance as the reference, guided by country availability, coin coverage, liquidity, and fees, plus testnet paper trading.

Compare Binance and FTX exchanges, highlighting international versus US versions, geographic availability, futures and spot trading, API access, testnet limitations, and fee structures with discounts.

Create accounts on FTX.com and FTX.us, complete identity verification, and fund your wallet. Explore spot and perpetual futures, and set up API keys for Python trading with CCed.

Learn to set up Python for automated crypto trading, install required packages with Anaconda, work in Jupyter Notebooks, and manage environments to prevent conflicts.

Install Anaconda to get Python and a full data science environment with preinstalled packages, supports Jupyter notebooks and IDEs like PyCharm or Spyder, across Windows, Mac, and Linux.

Open the Anaconda Navigator, launch Jupyter Notebook, create your first notebook, and run simple Python calculations while exploring the base environment and its packages.

Discover how to use Jupyter notebooks for interactive Python coding, including edit and command modes, markdown, headers, running cells with shortcuts, and common beginner pitfalls.

Start Python with Anaconda and Jupyter notebooks; use the appendix for a crash course on basics, for beginners, finance concepts, and libraries like NumPy, Pandas, and med plot lib.

Develop your debugging skills to quickly identify and fix more than 90% of errors in less than a minute through trial and error, reading, and common sense.

Improve your Python debugging skills by identifying and fixing common errors in a dictionary dataset, from key and type errors to improper indexing, and converting to a pandas series.

Explore the three major causes of coding errors: code problems, Python installation issues, and external factors, with a focus on corrupted code, typos, and improper inputs.

Identify and fix common, simple Python errors that derail code, from dictionary key mistakes and typos to misused brackets, missing quotes, and undefined variables.

Explore how omitting cells and changing the sequence of cells in pandas affects dataframes and indexing, with debugging tips and kernel restarts to fix key and name errors.

Learn how index errors occur in pandas data frames and lists, using iloc to access out of bounds positions and negative indexing, and inspecting previous cells to identify root cause.

Learn to debug Python indentation errors by identifying unexpected indents and missing required indents in dictionaries and for loops, including notebook cell scenarios, to ensure runnable code.

Identify why misusing Python keywords and built-in function names breaks code. Learn safeguards with examples converting tuples to lists and avoiding assigning names like list to a variable.

Learn how Python distinguishes type errors and value errors, with practical examples of incompatible operations, invalid value conversions, and the square root of negative numbers.

Explore how coders rely on reading error messages and using Stack Overflow and Google to resolve issues, including understanding positional vs keyword arguments in pandas and Python.

Master Python tracebacks to quickly locate errors in backtesting code, tracing from test_strategy to calculate_annualized_mean in a pandas, numpy, and plot lib workflow, and spot simple typos.

Identify and fix Python installation issues in Anaconda by checking missing packages, outdated versions, or corrupted setups, using conda and pip commands, avoiding multiple installations, and reinstalling when needed.

Identify how external factors such as weak internet connectivity, server errors, API key issues, and country restrictions affect data scraping, trading, and API calls, and follow troubleshooting and question-and-answer guidance.

Transcription errors are the main culprits in this course, so download the notebooks, watch the videos, run the code, and build your own solutions rather than copying line by line.

Use a practical debugging flowchart to quickly fix errors in Python notebooks. Read the error, inspect code, run all cells, update installations, and consult Q&A.

Load market data from Coingecko's free api with python and the pycoingecko wrapper, handling a 50 calls per minute rate limit to access coins, price, market cap, and historical data.

Explore cross-sectional, time series, and panel data structures for crypto analytics, including examples with Bitcoin, Ethereum, and Tether, and learn to analyze these data in Python using pandas.

Download and unzip the course materials, then load the cross sectional CSV into pandas using the notebooks and locate datasets in the same folder.

Learn to analyze a cross-sectional crypto dataset from coingecko using pandas, loading a cleaned csv, inspecting almost 9400 rows and ten columns, and understanding survivorship bias.

Use pandas to inspect crypto data and verify market cap equals price times circulating supply. Note zero market caps, missing values, and Bitcoin-led concentration with top 10 coins delivering 76.37%.

Build a Python tool in Jupyter to analyze crypto data, sorting by market cap, price, and circulating supply, and present logo tables, highlighting market cap as the indicator of value.

Learn to pull and clean panel time series crypto data from Coingecko, using pandas and matplotlib to build a long-format dataset covering 2017–2021, with Bitcoin examples.

Create price charts with pandas by setting the date as the index and plotting bitcoin prices from 2017 to 2022, highlighting volatility and trend.

Visualize Bitcoin market cap over time using a plot and compare it to price on dual axes. Interpret market cap versus price and circulating supply dynamics for investors.

Analyze bitcoin market share over time using python and pandas by computing bitcoin and total market caps from panel data, and visualize the market share trend.

Pivot to wide format from long data, set a datetime index, and compare coin performance by plotting prices and market caps, using a secondary y-axis for clarity.

Explore the CoinGecko API and the pycoingecko wrapper to load market data into Python, covering free access, rate limits, and calls for price, market cap, and historical data.

Install the pycoingecko package, update your environment with conda, import and connect to Coingecko API, verify connectivity with a ping, and prepare pandas for the next lectures before pulling data.

Explore simple calls on the Coingecko API to fetch current prices for Bitcoin in USD or EUR, and extend to multiple coins with 24h volume and price change.

Learn to use the coins list and coins markets calls from Coingecko to fetch thirteen thousand coins, then convert to a panda dataframe for market cap, price, and ranking analysis.

Fetch current and historical Bitcoin data via coin id and coin history by id, then build a time series of price, market cap, and volume.

Pull exchange data from coingecko using exchange calls like get exchanges and get exchanges list to identify top exchanges by trust score and access their tickers by id.

Load the cross-sectional cryptocurrency dataset from coingecko, fetch 250 coins per page to build a 13,398-coin dataframe, and prepare for cleaning in upcoming lectures.

Learn practical data cleaning of a cross-sectional crypto dataset: handle missing values, convert dates, remove duplicates, and decide when to keep or drop price information for analysis.

Clean the cross-sectional dataset by filtering market cap, circulating supply, and price inconsistencies; replace zero circulating supply with missing values and export the cleaned data to CSV.

Load all Binance-traded coins from CoinGecko, map base and target currencies, account for rate limits, and reveal 365 unique base coins and 354 altcoins purchasable with tether or bitcoin.

Load time series data for 365 coins using get coin market chart by id, creating a panel data frame of price, market cap, volume, symbol, and date.

Clean and prepare the panel data by converting columns to numeric, handling missing values with forward fill, aligning timestamps to midnight, and checking for duplicates to ensure a consistent dataset.

Compute the maximum market cap per coin, filter out coins with zero max market cap, and pivot the panel data to a daily wide format for crypto market analysis.

Clean and convert the panel data from long to wide, addressing missing values with forward fill. Save the cleaned data as csv for time-series analysis.

Build a Python portfolio investing and rebalancing bot for rapid multi-coin trading on Binance and FDX, covering API trading, error handling, and object oriented programming.

Explore the 60 Python library as a universal API wrapper for crypto trading, enabling historical and real-time data, order placement, and cross-exchange support.

Install the required library with pip in the correct shell, then import it. Inspect the exchanges attribute to view binance variants, fdx, and coinbase, with API connections coming next.

Connect to Binance api via ccxt and fetch ticker data for btc/usdt with 24-hour stats. The lecture covers variations across Binance futures and other exchanges, with sanity checks when switching.

Explore pulling general exchange information from an exchange API using Binance, including load markets, time frames, currency pairs, and basic fee data.

Explore the public api to pull market and exchange data without credentials. Use fetch ticker, fetch tickers, candlesticks, open-high-low-close-volume, and order book methods.

Pull Binance API historical open, high, low, close, and volume data to create candlestick charts, loading last 1000 bars and converting unix time to ISO 8601 UTC.

Load historical data in batches of 1000 until the current bar, using timestamp checks, deduplication, and a VI loop, then convert to a dataframe with a daytime index.

Learn how to create a Binance api key and secret key, copy them into your python notebook, and configure reading access with ip restrictions.

Learn how the private API handles your account data, including authentication with API key and secret, retrieving balances, trades, and orders, and submitting orders.

Explore the Binance spot test network to practice API spot trading using a GitHub login, generate an API key and secret, and run tests with the Python wrapper.

Connect to Binance testnets using sandbox mode to enable paper trading, fetch balances, and practice with API keys in a safe, simulated trading environment.

Learn to create market orders on the Binance testnet with the Krk library, buying and selling Bitcoin with USDT, handling amount and price parameters, and verifying orders and balances.

Explore ccxt and ftx differences across exchanges, connect to ftx with api keys and subaccounts, and compare balance and order data handling between exchanges.

Explain exception handling concepts, why to handle errors only when necessary, avoid overuse, and test code without error handling before adding safeguards for API and connectivity issues.

Learn how Python handles errors by iterating a list of integers with a stray string, triggering a type error, and using exception handling to keep the program running.

Use try and except to handle errors in Python loops without stopping the program. Test additions for type errors, print results, and choose actions like pass when exceptions arise.

learn to catch specific errors in python with try and except, handling type errors, value errors, and syntax errors, and know when a general catch-all is appropriate.

Capture Python errors with except as e, print the exception instance for details, and see how '4 is not a number' and 'unsupported operand type for addition' reveal information.

Learn how to use try, except, and else in Python to handle errors in cryptocurrency portfolio investing, keep the try block clean, and place success logic in the else block.

Explore Python's try, except, and finally blocks, showing how finally executes regardless of errors, enabling cleanup and continuing programs after handling invalid input or breaks.

Learn the retry until it works pattern for handling random errors from API calls, using a VI loop with try-except, finally, and counting attempts until success.

Enforce a maximum of three retries to prevent infinite loops, using a success flag and error checks to stop the VI loop.

Implement waiting periods between retries for temporary errors using time.sleep, starting at one second and increasing by five seconds after each failure, with a max four attempts.