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ML Strategies with PortfolioTesteR (Advanced Getting Started)

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Why ML in PortfolioTesteR

PortfolioTesteR lets you test ML-based stock selection with the same workflow you already know:

  1. Prepare lagged, leakage-safe features + future labels
  2. Fit -> Predict on a rolling IS/OOS schedule
  3. Transform -> Select -> Weight the scores
  4. Backtest and inspect diagnostics

All examples use the bundled weekly/daily datasets; no internet is required.

suppressPackageStartupMessages(library(PortfolioTesteR))
suppressPackageStartupMessages(library(data.table))

# Bundled sample data
data(sample_prices_weekly)
data(sample_prices_daily)

# Optional peek
head(sample_prices_weekly[, 1:6])
#> Key: <Date>
#>          Date     AAPL    AMZN       BA      BAC      CAT
#>        <Date>    <num>   <num>    <num>    <num>    <num>
#> 1: 2017-01-06 27.23376 39.7995 147.5085 18.70115 76.05074
#> 2: 2017-01-13 27.49475 40.8570 147.2582 18.97326 77.22778
#> 3: 2017-01-20 27.71648 40.4165 147.9072 18.66817 77.95100
#> 4: 2017-01-27 28.16688 41.7885 155.4819 19.26185 81.58563
#> 5: 2017-02-03 29.81370 40.5100 150.5680 19.20414 76.87957
#> 6: 2017-02-10 30.64816 41.3730 155.4447 19.03097 79.37682

1) Baseline ML (pooled): Ridge regression

Simple pooled regression on tabular features. The helpers build lagged features and future labels (4-week horizon).

# Prepare tabular features and labels
X <- ml_prepare_features(sample_prices_weekly, sample_prices_daily)
Y <- make_labels(sample_prices_weekly, horizon = 4L, type = "log")

# Linear/Ridge baseline
ridge <- ml_make_model("ridge")

set.seed(1)
res_baseline <- ml_backtest(
  features_list = X, labels = Y,
  fit_fn  = ridge$fit,
  predict_fn = ridge$predict,
  schedule = list(is = 104L, oos = 4L, step = 4L),  # 2y IS, 1m OOS, monthly step
  transform = "zscore",                              # IS-only scaling (no leakage)
  selection = list(top_k = 15L),
  weighting = list(method = "rank"),
  prices = sample_prices_weekly,
  initial_capital = 1e5,
  name = "Baseline Ridge (pooled)"
)

print(res_baseline$backtest)
#> Backtest Result:  Baseline Ridge (pooled) 
#> =====================================
#> Warmup Period: 104 observations (no trading)
#> Active Period: 2018-12-31 to 2019-12-31 (54 observations)
#> Initial Capital: $1e+05
#> Final Value: $135,743
#> Total Return (active period): 35.74%
#> Total Return (full period): 35.74%
#> Transactions: 784
#> 
#> Annualized Return: 34.22%
#> Annualized Volatility: 11.74%
#> Sharpe Ratio: 2.91
#> Max Drawdown: -6.08%
plot(res_baseline$backtest, type = "performance")

2) Ensemble: Ridge + XGBoost

This section is optional. It runs only if at least one of ranger/xgboost is installed.

# Start with ridge (always available)
models <- list(ml_make_model("ridge"))

# Add RF only if ranger is installed
if (has_rf) {
  models <- c(models, list(ml_make_model("rf", num.trees = if (heavy) 400L else 200L, mtry = 3L)))
}

# Add XGBoost only if xgboost is installed
if (has_xgb) {
  models <- c(models, list(
    ml_make_model(
      "xgboost",
      params  = list(objective = "reg:squarederror", max_depth = 4L, eta = 0.07),
      nrounds = if (heavy) 300L else 150L
    )
  ))
}

# Build the ensemble from whatever is available
ens <- do.call(ml_make_ensemble, models)

set.seed(2)
res_ens <- ml_backtest(
  features_list = X, labels = Y,
  fit_fn = ens$fit, predict_fn = ens$predict,
  schedule = list(is = 104L, oos = 4L, step = 4L),
  transform = "zscore",
  selection = list(top_k = 15L),
  weighting = list(method = "rank"),
  prices = sample_prices_weekly,
  initial_capital = 1e5,
  name = "Ensemble (available learners)"
)

print(res_ens$backtest)
#> Backtest Result:  Ensemble (available learners) 
#> =====================================
#> Warmup Period: 104 observations (no trading)
#> Active Period: 2018-12-31 to 2019-12-31 (54 observations)
#> Initial Capital: $1e+05
#> Final Value: $133,347
#> Total Return (active period): 33.35%
#> Total Return (full period): 33.35%
#> Transactions: 872
#> 
#> Annualized Return: 31.93%
#> Annualized Volatility: 12.13%
#> Sharpe Ratio: 2.63
#> Max Drawdown: -7.33%
plot(res_ens$backtest, type = "performance")

3) Sector-neutral modelling (tabular XGBoost, per-group)

This section is optional and runs only if xgboost is installed.

symbols <- setdiff(names(sample_prices_weekly), "Date")
gmap    <- demo_sector_map(symbols, n_groups = 4L)  # demo mapping for the sample data

xgb_g <- ml_make_model(
  "xgboost",
  params  = list(objective = "reg:squarederror", max_depth = 3L, eta = 0.05),
  nrounds = if (heavy) 250L else 150L
)

set.seed(3)
res_xgb_sect <- ml_backtest(
  features_list = X, labels = Y,
  fit_fn = xgb_g$fit, predict_fn = xgb_g$predict,
  schedule = list(is = 104L, oos = 4L, step = 4L),
  group = "per_group", group_map = gmap,   # sector-neutral training
  transform = "zscore",
  selection = list(top_k = 15L),
  weighting = list(method = "softmax", temperature = 12),
  prices = sample_prices_weekly,
  initial_capital = 1e5,
  name = "XGBoost (per-sector neutral)"
)

print(res_xgb_sect$backtest)
#> Backtest Result:  XGBoost (per-sector neutral) 
#> =====================================
#> Warmup Period: 104 observations (no trading)
#> Active Period: 2018-12-31 to 2019-12-31 (54 observations)
#> Initial Capital: $1e+05
#> Final Value: $129,909
#> Total Return (active period): 29.91%
#> Total Return (full period): 29.91%
#> Transactions: 870
#> 
#> Annualized Return: 28.66%
#> Annualized Volatility: 10.95%
#> Sharpe Ratio: 2.62
#> Max Drawdown: -6.74%
plot(res_xgb_sect$backtest, type = "performance")

4) Sequence model (GRU, pooled): “returns pyramid”

Optional and disabled by default. Enable locally by running Sys.setenv(RUN_SEQ = "true") before knitting, and only if your installation supports sequence models.

tf_autograph_scope()  # isolate TF Autograph cache to avoid detritus NOTE

# Build a compact 'returns pyramid' of momentum-like sequences (lagged)
r1  <- panel_lag(calc_momentum(sample_prices_weekly,  1L), 1L)
r4  <- panel_lag(calc_momentum(sample_prices_weekly,  4L), 1L)
r12 <- panel_lag(calc_momentum(sample_prices_weekly, 12L), 1L)

features_seq <- list(r1 = r1, r4 = r4, r12 = r12)
Y_seq <- make_labels(sample_prices_weekly, horizon = 4L, type = "log")

units  <- if (heavy) 32L else 8L
epochs <- if (heavy) 12L else 4L

seq_gru <- ml_make_seq_model(
  "gru",
  steps  = 26L,
  units  = units,
  epochs = epochs,
  seed   = 42L
)

set.seed(42)
res_seq_gru <- ml_backtest_seq(
  features_list   = features_seq,
  labels          = Y_seq,
  steps           = 26L,
  horizon         = 4L,
  fit_fn          = seq_gru$fit,
  predict_fn      = seq_gru$predict,
  schedule        = list(is = 104L, oos = 4L, step = 4L),
  group           = "pooled",
  normalize       = "zscore",
  selection       = list(top_k = 10L),
  weighting       = list(method = "softmax", temperature = 12),
  prices          = sample_prices_weekly,
  initial_capital = 1e5,
  name            = "Seq-GRU (pooled): returns pyramid (26x3)"
)

print(res_seq_gru$backtest)
plot(res_seq_gru$backtest, type = "performance")

5) Sequence model (GRU, per-group): sector-neutral sequences

Optional and disabled by default. Enable with RUN_SEQ="true".

tf_autograph_scope()  # isolate TF Autograph cache for this chunk too

symbols <- setdiff(names(sample_prices_weekly), "Date")
gmap    <- demo_sector_map(symbols, n_groups = 4L)

units_g  <- if (heavy) 32L else 8L
epochs_g <- if (heavy) 12L else 4L

seq_gru_g <- ml_make_seq_model(
  "gru",
  steps  = 26L,
  units  = units_g,
  epochs = epochs_g,
  seed   = 123L
)

set.seed(123)
res_seq_gru_g <- ml_backtest_seq(
  features_list   = features_seq,
  labels          = Y_seq,
  steps           = 26L,
  horizon         = 4L,
  fit_fn          = seq_gru_g$fit,
  predict_fn      = seq_gru_g$predict,
  schedule        = list(is = 104L, oos = 4L, step = 4L),
  group           = "per_group", group_map = gmap,
  normalize       = "zscore",
  selection       = list(top_k = 10L),
  weighting       = list(method = "softmax", temperature = 12),
  prices          = sample_prices_weekly,
  initial_capital = 1e5,
  name            = "Seq-GRU (per-sector neutral): returns pyramid (26x3)"
)

print(res_seq_gru_g$backtest)
plot(res_seq_gru_g$backtest, type = "performance")

6) Light tuning: Top-K and softmax temperature

Runs only off-CRAN (when NOT_CRAN=true).

topk_vals <- c(8L, 10L, 12L, 15L)
temp_vals <- c(8, 12, 16)

score_tbl <- tune_ml_backtest(
    features_list = X, labels = Y, prices = sample_prices_weekly,
    fit_fn = ridge$fit, predict_fn = ridge$predict,
    schedule = list(is = 104L, oos = 4L, step = 4L),
    grid = list(
        top_k = topk_vals,
        temperature = temp_vals,
        method = "softmax",
        transform = "zscore"
    )
)

score_tbl[order(-sharpe)][1:10]

Recap & Next Steps

Try next:
1) Replace GRU with LSTM/CNN1D if available (still gated by RUN_SEQ).
2) Blend tabular and sequence scores (e.g., average ranks) before weighting.
3) Add exposure caps or per-sector position limits when selecting Top-K.

sessionInfo()
#> R version 4.2.1 (2022-06-23 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 26200)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=C                           
#> [2] LC_CTYPE=English_United Kingdom.utf8   
#> [3] LC_MONETARY=English_United Kingdom.utf8
#> [4] LC_NUMERIC=C                           
#> [5] LC_TIME=English_United Kingdom.utf8    
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] data.table_1.16.0     PortfolioTesteR_0.1.3
#> 
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_1.0.13       bslib_0.9.0       compiler_4.2.1    jquerylib_0.1.4  
#>  [5] iterators_1.0.14  tools_4.2.1       xts_0.14.1        digest_0.6.37    
#>  [9] jsonlite_2.0.0    evaluate_1.0.5    lifecycle_1.0.4   lattice_0.20-45  
#> [13] rlang_1.1.6       Matrix_1.6-5      foreach_1.5.2     cli_3.6.1        
#> [17] rstudioapi_0.17.1 curl_7.0.0        yaml_2.3.10       xfun_0.53        
#> [21] fastmap_1.2.0     ranger_0.16.0     knitr_1.50        sass_0.4.10      
#> [25] xgboost_1.7.11.1  grid_4.2.1        glmnet_4.1-10     R6_2.6.1         
#> [29] survival_3.3-1    rmarkdown_2.29    TTR_0.24.4        codetools_0.2-18 
#> [33] htmltools_0.5.8.1 splines_4.2.1     shape_1.4.6.1     cachem_1.1.0     
#> [37] zoo_1.8-12

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.
Health stats visible at Monitor.