Package: tidyquant 1.0.12.9000

Overview | Prerequisites | 1.0 Retrieve Consolidated Symbol Data | 1.1 Stock Indexes | 1.2 Stock Exchanges | 1.0 Get Quantitative Data | 2.1 Yahoo! Finance | 2.2 FRED Economic Data | 2.3 Nasdaq Data Link (Quandl) API | Authentication | Search | Getting Nasdaq Data Link Data | 2.4 Tiingo API | Getting Tiingo Data | 2.5 Alpha Vantage API | Getting Alpha Vantage Data | 2.6 Bloomberg | Getting Bloomberg Data | 3.0 Mutate Quantitative Data | 3.1 Transmute Quantitative Data, tq_transmute | Working with non-OHLC data | 3.2 Mutate Quantitative Data, tq_mutate | Mutate rolling regressions with rollapply | 3.3 _xy Variants, tq_mutate_xy and tq_transmute_xy | Mutate with two primary inputs

Overview | 1.0 Key Concepts | 2.0 Quick Example | 3.0 Workflow | 3.1 Individual Assets | Step 1A: Get stock prices | Step 2A: Mutate to returns | Step 3A: Aggregate to Portfolio Returns (Skipped) | Step 4: Analyze Performance | 3.2 Portfolios (Asset Groups) | Single Portfolio | Steps 1A and 2A: Asset Period Returns | Steps 1B and 2B: Baseline Period Returns | Step 3A: Aggregate to Portfolio Period Returns | Method 1: Aggregating a Portfolio using Vector of Weights | Method 2: Aggregating a Portfolio using Two Column tibble with Symbols and Weights | Step 3B: Merging Ra and Rb | Step 4: Computing the CAPM Table | Multiple Portfolios | Steps 1 and 2 are the Exact Same as the Single Portfolio Example | Step 3A: Aggregate Portfolio Returns for Multiple Portfolios | Steps 3B and 4: Merging and Assessing Performance | 4.0 Available Functions | 4.1 table.Stats | 4.2 table.CAPM | 4.3 table.AnnualizedReturns | 4.4 table.Correlation | 4.5 table.DownsideRisk | 4.6 table.DownsideRiskRatio | 4.7 table.HigherMoments | 4.8 table.InformationRatio | 4.9 table.Variability | 4.10 VaR | 4.11 SharpeRatio | 5.0 Customizing using the ... | 5.1 Customizing tq_portfolio | 5.2 Customizing tq_performance

Overview | Prerequisites | Chart Types | Line Chart | Bar Chart | Candlestick Chart | Charting Multiple Securities | Visualizing Trends | Moving Averages | Example 1: Charting the 50-day and 200-day simple moving average | Example 2: Charting exponential moving averages | Example 3: Charting moving averages for multiple stocks at once | Bollinger Bands | Example 1: Applying BBands using a SMA | Example 2: Modifying the appearance of Bollinger Bands | Example 3: Adding BBands to multiple stocks | ggplot2 Functionality | Example 1: Log Scale with scale_y_log10 | Example 2: Regression trendlines with geom_smooth | Example 3: Charting volume with geom_segment | Themes | Dark

2-Minutes To Tidyquant | Benefits | A Few Core Functions with A Lot of Power | Integrates the Quantitative Analysis Functionality of xts/zoo, quantmod TTR and Performance Analytics | Designed for the data science workflow of the tidyverse | Implements ggplot2 Functionality for Financial Visualizations | Performance Analysis of Asset and Portfolio Returns

Overview | Prerequisites | 1.0 Function Compatibility | zoo Functionality | xts Functionality | quantmod Functionality | TTR Functionality | PerformanceAnalytics Functionality | 2.0 Quantitative Power In Action | Example 1: Use quantmod periodReturn to Convert Prices to Returns | Example 1A: Getting and Charting Annual Returns | Example 1B: Getting Daily Log Returns | Example 2: Use xts to.period to Change the Periodicity from Daily to Monthly | Without Periodicity Aggregation | With Monthly Periodicity Aggregation | Example 3: Use TTR runCor to Visualize Rolling Correlations of Returns | Example 4: Use TTR MACD to Visualize Moving Average Convergence Divergence | Example 5: Use xts apply.quarterly to Get the Max and Min Price for Each Quarter | Example 6: Use zoo rollapply to visualize a rolling regression | Example 7: Use Return.clean and Return.excess to clean and calculate excess returns

Overview | Prerequisites | 1.0 Scaling the Getting of Financial Data | Method 1: Map a character vector with multiple stock symbols | Method 2: Map a tibble with stocks in first column | Method 2A: Make a tibble | Method 2B: Use index or exchange | 2.0 Scaling the Mutation of Financial Data | 3.0 Modeling Financial Data using purrr | Example: Applying a Regression Model to Detect a Positive Trend | Analyze a Single Stock | Scale to Many Stocks | 4.0 Error Handling when Scaling | Pros and Cons to Built-In Error-Handling | Bad Apples Fail Gracefully, tq_get