Mastering Coinbase Advance Trade API Python

Learn to use the Coinbase rest client in a Jupyter notebook to fetch all accounts, retrieve details by account UUID, and synchronize time with Unix time and epoch formats.

Learn to place orders with Coinbase advance trade api Python by specifying code size as a string, executing market orders, limit orders, and cancellations, and reviewing orders and trades.

Learn to fetch btc/usd product details, access the price book with bids and asks, manage portfolios, view market trades, and explore websockets for live tick data.

Explore websockets to stream live candle data from Coinbase using JWT authentication, subscribing to currency pairs and channels like candles and level two in Python.

Learn the basics of technical indicators from price and volume data to forecast trends. Code them in Python and use overlays and oscillators like MACD, RSI, EMA, and Bollinger bands.

Compute macd as the difference between the 12-day and 26-day exponential moving averages, with a 9-day signal line. Crossovers mark bullish or bearish trend reversals.

Explore Bollinger bands as a volatility indicator built from a 20-day moving average and upper and lower bands at two standard deviations, with calculations in Google Sheets and Python.

Learn how average true range measures volatility, contrasting with Bollinger Bands, by tracking moves, computing true range from high, low, and previous close, then applying a 14-day EMA in Python.

Explore the relative strength indicator (rsi), a 0–100 momentum oscillator for oversold and overbought signals and trend reversals. Learn to compute it with gains and losses.

Analyze price data to compute daily gains and losses, then derive 14-day average gains and losses, calculate relative strength and RSI, and plot close price with RSI to visualize momentum.

Explore the super trend indicator, a trend following tool using atr to compute upper and lower bands, signaling trend reversals in intraday trading.

Learn to compute the supertrend in Google Sheets using true range, seven-period ATR, and an exponential moving average, building upper and lower bands and identifying trend reversals for Python implementation.

Learn to compute the supertrend in python using ATR with a 7 period and a 3 multiplier, derive the basic and final upper bounds, and detect trend changes.

Explore Renko charts, a price-movement visualization where a new brick forms after a defined price change and brick size, filtering noise and ignoring fixed time intervals.

Learn to generate Renko bricks in Python by calculating brick counts from price changes and brick size, then visualize or feed bricks into an algo trading strategy.

Learn to use google sheets to calculate adx values, including true range, dm plus and dm minus, and dx with a 14-bar smoothing.