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End-to-End Workflow

library(finlabR)

1. Load prices and compute returns

prices <- get_example_prices()
rets <- calc_returns(prices[, -1], method = "log")

2. Correlation analysis across assets

cor_mat <- asset_correlation(rets)
cor_mat[1:3, 1:3]
#>              EQ1          EQ2         EQ3
#> EQ1  1.000000000  0.006707895 -0.02176708
#> EQ2  0.006707895  1.000000000 -0.01836227
#> EQ3 -0.021767078 -0.018362273  1.00000000

3. Mean-variance optimization

ef <- mvo_efficient_frontier(rets, n = 25, rf = 0.02)
max_sharpe <- mvo_max_sharpe(rets, rf = 0.02)
min_var <- mvo_min_variance(rets)

4. CVaR and risk parity portfolios

cvar <- cvar_minimize(rets, alpha = 0.95)
rp <- risk_parity_weights(stats::cov(rets))

5. Regime clustering and asset clustering

regimes <- market_regime_kmeans(rets, k = 3, window = 60)
clusters <- asset_clustering(rets, method = "kmeans", reduce = "pca", k = 3)

6. Risk analytics

var_cvar(rets, alpha = 0.95)
#>     asset         VaR        CVaR
#> 1     EQ1 0.018348805 0.023653700
#> 2     EQ2 0.015278103 0.019074063
#> 3     EQ3 0.024202496 0.029746296
#> 4    CMD1 0.025586040 0.032379499
#> 5 CRYPTO1 0.086507269 0.107429605
#> 6   BOND1 0.005233202 0.006338586

7. Monte Carlo and option pricing

paths <- simulate_gbm_paths(100, 0.08, 0.2, time_horizon = 1, n_steps = 252, n_sims = 500)
price_option_mc(100, 100, 0.02, 0.2, time_to_maturity = 1, n_sims = 10000)
#> $price
#> [1] 9.065308
#> 
#> $std_error
#> [1] 0.1414864
price_option_binomial(100, 100, 0.02, 0.2, time_to_maturity = 1, n_steps = 200, american = TRUE)
#> [1] 8.906137

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.