Intro to Quant Algorithms w/ R & Alphien

Explore a Python notebook template for Bollinger bands around a moving average, MACD and RSI signals, with backtesting, data merging, and customizable plots in a forex-oriented toolkit, including candlesticks.

Examine CAPM models to compare a portfolio's beta and alpha against the realized market return, using symbols, close-price data, and linear modeling in Alphien.

Develop and submit a combined long only strategy using simple moving average components, calibrate and finalize parameters, backtest performance, and potentially submit to Alphien leaderboards.

Load sources from Alpha to create a euro usd overlay plot, extract data from the index, and set the zoom from 2014 to rebase the euro overlay.

learn momentum strategy by ranking currency pairs with the roic mass method, generate trading signals from monthly data, and backtest the basket to compare currency performance.

Identify and assemble a portfolio of strategies by constructing building blocks, applying moving averages, ROIC analyses, and backtesting; optimize parameters and combine strategies with validation.

Learn to create a long only trading strategy using building blocks, moving averages, and two parameterized strategies, then combine them with weighted back test to finalize the result.

Explore how the z-score model analyzes forex spreads using moving averages and backtesting to build mean-reverting indicators for predictive analysis.

Explore options data with Alphien in R, fetch data fields and volume surface, set expiry and delta or money targets, and plot volatility surfaces and expiration strangles for tickers.

Learn to use source qlib to access alpha and packets, avoiding the deprecated source alpha exit. Stay ahead of upcoming deprecations in Python setups.

Explore Alphien's essential tools, including loading Alphien and its packages, reference and time-series data, building blocks for stocks and fx, back testing and paper trading, and Alphien view charts.