PortfolioTesteR: Test Investment Strategies with English-Like Code

Description

Design, backtest, and analyze portfolio strategies using simple, English-like function chains. Includes technical indicators, flexible stock selection, portfolio construction methods (equal weighting, signal weighting, inverse volatility, hierarchical risk parity), and a compact backtesting engine for portfolio returns, drawdowns, and summary metrics.

Author(s)

Maintainer: Alberto Pallotta pallottaalberto@gmail.com

See Also

Useful links:

• https://github.com/alb3rtazzo/PortfolioTesteR

• Report bugs at https://github.com/alb3rtazzo/PortfolioTesteR/issues

Create Window Splits for Walk-Forward

Description

Create Window Splits for Walk-Forward

Usage

.wf_make_splits(n, is_periods, oos_periods, step)

Align Data to Strategy Timeframe

Description

Aligns higher-frequency data to match strategy timeframe.

Usage

align_to_timeframe(
  high_freq_data,
  low_freq_dates,
  method = c("forward_fill", "nearest", "interpolate")
)

Arguments

Value

Aligned data frame

Examples

data("sample_prices_weekly")
data("sample_prices_daily")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, 10)
# Create a stability signal from daily data
daily_vol <- calc_rolling_volatility(sample_prices_daily, lookback = 20)
stability_signal <- align_to_timeframe(daily_vol, sample_prices_weekly$Date)
weights <- weight_by_signal(selected, stability_signal)

Analyze Drawdown Characteristics

Description

Detailed analysis of drawdown periods including depth, duration, and recovery.

Usage

analyze_drawdowns(drawdowns, returns)

Arguments

Value

List with drawdown statistics

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, n = 10)
weights <- weight_equally(selected)
result <- run_backtest(sample_prices_weekly, weights)
dd_analysis <- analyze_drawdowns(result$portfolio_value, result$dates)

Analyze Backtest Performance with Daily Monitoring

Description

Calculates comprehensive performance metrics using daily price data for enhanced accuracy. Provides risk-adjusted returns, drawdown analysis, and benchmark comparison even when strategy trades at lower frequency.

Usage

analyze_performance(
  backtest_result,
  daily_prices,
  benchmark_symbol = "SPY",
  rf_rate = 0,
  confidence_level = 0.95
)

Arguments

Value

performance_analysis object with metrics and daily tracking

Examples

data("sample_prices_weekly")
data("sample_prices_daily")

# Use overlapping symbols; cap to 3
syms_all <- intersect(names(sample_prices_weekly)[-1], names(sample_prices_daily)[-1])
stopifnot(length(syms_all) >= 1)
syms <- syms_all[seq_len(min(3L, length(syms_all)))]

# Subset weekly (strategy) and daily (monitoring) to the same symbols
P <- sample_prices_weekly[, c("Date", syms), with = FALSE]
D <- sample_prices_daily[,  c("Date", syms), with = FALSE]

# Simple end-to-end example
mom <- calc_momentum(P, lookback = 12)
sel <- filter_top_n(mom, n = 3)
W   <- weight_equally(sel)
res <- run_backtest(P, W)

# Pick a benchmark that is guaranteed to exist in D
perf <- analyze_performance(res, D, benchmark_symbol = syms[1])
print(perf)
summary(perf)

Analyze Performance Against Benchmark

Description

Calculates benchmark-relative metrics including alpha, beta, tracking error, information ratio, and correlation.

Usage

analyze_vs_benchmark(
  portfolio_returns,
  benchmark_prices,
  dates,
  benchmark_symbol = "SPY"
)

Arguments

Value

List of benchmark comparison metrics

Apply Market Regime Filter

Description

Applies regime-based filtering. When regime is FALSE (e.g., bear market), all selections become 0, moving portfolio to cash.

Usage

apply_regime(selection_df, regime_condition, partial_weight = 0)

Arguments

Value

Modified selection matrix respecting regime

Examples

data("sample_prices_weekly")
# Create selection
momentum <- calc_momentum(sample_prices_weekly, 12)
selected <- filter_top_n(momentum, 10)

# Only trade when SPY above 20-week MA
ma20 <- calc_moving_average(sample_prices_weekly, 20)
spy_regime <- sample_prices_weekly$SPY > ma20$SPY
spy_regime[is.na(spy_regime)] <- FALSE

regime_filtered <- apply_regime(selected, spy_regime)

Apply Weighting Method to Values

Description

Internal utility function that applies various weighting methods to a vector. Used by other weighting functions.

Usage

apply_weighting_method(values, method = "rank", preference_ascending = TRUE)

Arguments

Value

Named numeric vector of weights that sum to 1

Convert Conditions to Selection Format

Description

Converts condition matrices or data frames to standard selection format with Date column and binary values. Handles NA by converting to 0.

Usage

as_selection(condition_matrix, date_column = NULL)

Arguments

Value

Data.table in selection format (Date + binary columns)

Examples

data("sample_prices_weekly")
ma20 <- calc_moving_average(sample_prices_weekly, 20)
above_ma <- filter_above(calc_distance(sample_prices_weekly, ma20), 0)
selection <- as_selection(above_ma, sample_prices_weekly$Date)

Calculate Comprehensive Backtest Metrics

Description

Computes performance metrics including Sharpe ratio, maximum drawdown, win rate, and other statistics from backtest results.

Usage

backtest_metrics(result)

Arguments

Value

List containing performance metrics

Examples

# Create a backtest result to use
data(sample_prices_weekly)
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, n = 10)
weights <- weight_equally(selected)
result <- run_backtest(sample_prices_weekly, weights)

# Calculate metrics
metrics <- backtest_metrics(result)
print(metrics$sharpe_ratio)

Bucketed label analysis by score rank

Description

Bucketed label analysis by score rank

Usage

bucket_returns(
  scores,
  labels,
  n_buckets = 10L,
  label_type = c("log", "simple")
)

Arguments

Value

data.table of per-date bucket returns; cumulative series attached as attr(result, "cum").

Calculate Commodity Channel Index (CCI)

Description

Calculates CCI using closing prices. CCI measures deviation from average price. Values above 100 indicate overbought, below -100 indicate oversold.

Usage

calc_cci(data, period = 20)

Arguments

Value

Data.table with CCI values

Examples

data("sample_prices_weekly")
cci <- calc_cci(sample_prices_weekly, period = 20)

Calculate Distance from Reference

Description

data("sample_prices_weekly") Calculates percentage distance between prices and reference values (typically moving averages).

Usage

calc_distance(price_df, reference_df)

Arguments

Value

Data.table with percentage distances

Examples

data("sample_prices_weekly")
ma20 <- calc_moving_average(sample_prices_weekly, 20)
data("sample_prices_weekly")
distance <- calc_distance(sample_prices_weekly, ma20)

Calculate Market Breadth Percentage

Description

Measures the percentage of stocks meeting a condition (market participation). Useful for assessing market health and identifying broad vs narrow moves.

Usage

calc_market_breadth(condition_df, min_stocks = 10)

Arguments

Value

A data.table with Date and Breadth_[Sector] columns (0-100 scale)

Examples

# Percent of stocks above 200-day MA
data("sample_prices_weekly")
ma200 <- calc_moving_average(sample_prices_weekly, 200)
above_ma <- filter_above(calc_distance(sample_prices_weekly, ma200), 0)
breadth <- calc_market_breadth(above_ma)

Calculate Price Momentum

Description

Calculates momentum as the percentage change in price over a specified lookback period. Optimized using column-wise operations (25x faster).

Usage

calc_momentum(data, lookback = 12)

Arguments

Value

Data.table with momentum values (0.1 = 10% increase)

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)

Calculate Moving Average

Description

Calculates simple moving average for each column in the data.

Usage

calc_moving_average(data, window = 20)

Arguments

Value

Data.table with moving average values

Examples

data("sample_prices_weekly")
ma20 <- calc_moving_average(sample_prices_weekly, window = 20)

Calculate Cross-Sectional Ranking of Indicators

Description

Ranks each stock's indicator value against all other stocks on the same date. Enables relative strength strategies that adapt to market conditions. Optimized using matrix operations for 15x speedup.

Usage

calc_relative_strength_rank(
  indicator_df,
  method = c("percentile", "rank", "z-score")
)

Arguments

Value

Data frame with same structure containing ranks/scores

Examples

# Rank RSI across all stocks
data("sample_prices_weekly")
rsi <- calc_rsi(sample_prices_weekly, 14)
rsi_ranks <- calc_relative_strength_rank(rsi, method = "percentile")

# Find relatively overbought (top 10%)
relative_overbought <- filter_above(rsi_ranks, 90)

Rolling correlation of each symbol to a benchmark

Description

Computes rolling correlations between each symbol and a benchmark series (e.g., SPY) using simple returns over a fixed lookback window.

Usage

calc_rolling_correlation(
  data,
  benchmark_symbol = "SPY",
  lookback = 60,
  min_periods = NULL,
  method = c("pearson", "spearman")
)

Arguments

Details

Returns are computed as simple returns (P_t - P_{t-1})/P_{t-1}. Windows with fewer than min_periods valid pairs are marked NA.

Value

A data.table with Date and one column per non-benchmark symbol, containing rolling correlations. Insufficient data yields NAs.

See Also

calc_momentum(), calc_rolling_volatility()

Examples

data(sample_prices_weekly)
corr <- calc_rolling_correlation(
  data = sample_prices_weekly,
  benchmark_symbol = "SPY",
  lookback = 20
)
head(corr)

Calculate Rolling Volatility

Description

Calculates rolling volatility using various methods including standard deviation, range-based, MAD, or absolute returns. Supports different lookback periods.

Usage

calc_rolling_volatility(data, lookback = 20, method = "std")

Arguments

Value

Data frame with Date column and volatility values for each symbol

Examples

data("sample_prices_weekly")
# Standard deviation volatility
vol <- calc_rolling_volatility(sample_prices_weekly, lookback = 20)
# Range-based volatility
vol_range <- calc_rolling_volatility(sample_prices_weekly, lookback = 20, method = "range")

Calculate Relative Strength Index (RSI)

Description

Calculates RSI for each column. RSI ranges from 0-100. Above 70 indicates overbought, below 30 indicates oversold.

Usage

calc_rsi(data, period = 14)

Arguments

Value

Data.table with RSI values (0-100 range)

Examples

data("sample_prices_weekly")
rsi <- calc_rsi(sample_prices_weekly, period = 14)
overbought <- filter_above(rsi, 70)

Calculate Market Breadth by Sector

Description

Measures participation within each sector separately, revealing which sectors have broad strength vs concentrated leadership. Optimized using pre-splitting for speed.

Usage

calc_sector_breadth(
  condition_df,
  sector_mapping,
  min_stocks_per_sector = 3,
  na_sector_action = c("exclude", "separate", "market")
)

Arguments

Value

A data.table with Date and Breadth_[Sector] columns (0-100 scale)

Examples

data("sample_prices_weekly")
data("sample_sp500_sectors")
ma200 <- calc_moving_average(sample_prices_weekly, 200)
above_ma <- filter_above(calc_distance(sample_prices_weekly, ma200), 0)
sector_breadth <- calc_sector_breadth(above_ma, sample_sp500_sectors)

Calculate Indicators Relative to Sector Average

Description

Measures how each stock's indicator compares to its sector benchmark. Enables sector-neutral strategies and identifies sector outperformers.

Usage

calc_sector_relative_indicators(
  indicator_df,
  sector_mapping,
  method = c("difference", "ratio", "z-score"),
  benchmark = c("mean", "median"),
  ratio_threshold = 0.01,
  min_sector_size = 2
)

Arguments

Value

Data frame with sector-relative values

Examples

# Find stocks outperforming their sector
data("sample_prices_weekly")
data("sample_sp500_sectors")
momentum <- calc_momentum(sample_prices_weekly, 12)
relative_momentum <- calc_sector_relative_indicators(
  momentum, sample_sp500_sectors, method = "difference"
)

Calculate Stochastic D Indicator

Description

Calculates the Stochastic D indicator for momentum analysis. The %D line is the smoothed version of %K, commonly used for momentum signals in range 0-100.

Usage

calc_stochastic_d(data, k = 14, d = 3)

Arguments

Value

Data.table with Stochastic D values for each symbol

Examples

data("sample_prices_weekly")
data(sample_prices_weekly)
data("sample_prices_weekly")
stoch_d <- calc_stochastic_d(sample_prices_weekly, k = 14, d = 3)
head(stoch_d)

Stochastic RSI (StochRSI) for multiple price series

Description

Computes Stochastic RSI (%K) per column over a rolling window, returning values in [0, 1]. For each symbol, RSI is computed with TTR::RSI() over rsi_length periods; then StochRSI is (RSI_t - \min RSI_{t-L+1:t}) / (\max RSI_{t-L+1:t} - \min RSI_{t-L+1:t}), where L is stoch_length. If the range is zero the value is handled per on_const_window (default "zero").

Usage

calc_stochrsi(
  data,
  length = 14L,
  rsi_length = NULL,
  stoch_length = NULL,
  on_const_window = c("zero", "na")
)

Arguments

Value

A data.table with Date and symbol columns containing StochRSI in [0, 1], with leading NAs for warmup.

See Also

TTR::RSI(), calc_momentum(), calc_moving_average(), filter_top_n(), weight_by_risk_parity()

Examples

data(sample_prices_weekly)
s <- calc_stochrsi(sample_prices_weekly, length = 14)
head(s)

Calculate Annualized Return

Description

Converts total return to annualized return based on time period.

Usage

calculate_annualized_return(result)

Arguments

Value

Annualized return as decimal (0.1 = 10%)

Optimized cluster variance calculation

Description

Optimized cluster variance calculation

Optimized cluster variance calculation

Usage

calculate_cluster_variance_optimized(cov_matrix, asset_names)

calculate_cluster_variance_optimized(cov_matrix, asset_names)

Calculate Drawdown Time Series

Description

Computes drawdown series from portfolio values.

Usage

calculate_drawdown_series(values)

Arguments

Value

Numeric vector of drawdowns (as negative percentages)

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
sel <- filter_top_n(momentum, n = 10)
W   <- weight_equally(sel)
res <- run_backtest(sample_prices_weekly, W)
dd_series <- calculate_drawdown_series(res$portfolio_values)
dd_stats  <- analyze_drawdowns(dd_series, res$returns)

Calculate Portfolio Drawdowns

Description

Calculates drawdown series from portfolio values. Drawdown is the percentage decline from the previous peak.

Usage

calculate_drawdowns(values)

Arguments

Value

Numeric vector of drawdown percentages (negative values)

Calculate Enhanced Performance Metrics

Description

Computes comprehensive risk and return metrics from daily data including Sharpe, Sortino, Calmar ratios, VaR, CVaR, and tail risk measures.

Usage

calculate_enhanced_metrics(
  daily_values,
  daily_returns,
  rf_rate = 0,
  confidence_level = 0.95
)

Arguments

Value

List of performance metrics

Calculate Equal Risk Contribution weights (simplified)

Description

Calculate Equal Risk Contribution weights (simplified)

Usage

calculate_erc_weights(cov_matrix, asset_names)

Calculate HRP weights for a given returns matrix

Description

Calculate HRP weights for a given returns matrix

Usage

calculate_hrp_weights(
  returns_matrix,
  asset_names,
  cluster_method,
  distance_method
)

Calculate Maximum Diversification Portfolio weights

Description

Calculate Maximum Diversification Portfolio weights

Usage

calculate_max_div_weights(cov_matrix, asset_names)

Apply post-weight exposure caps

Description

Apply post-weight exposure caps

Usage

cap_exposure(
  weights,
  caps = list(max_per_symbol = 0.1, max_per_group = 0.25),
  group_map = NULL,
  renorm = TRUE
)

Arguments

Cap turnover sequentially across dates

Description

Cap turnover sequentially across dates

Usage

cap_turnover(weights, max_turnover = 0.2)

Arguments

Value

executed weights after turnover capping.

Carry-forward weights between rebalances (validation helper)

Description

Carry-forward weights between rebalances (validation helper)

Usage

carry_forward_weights(weights)

Arguments

Value

weight panel with rows filled forward and each active row sum=1.

Combine Multiple Filter Conditions

Description

Combines multiple filter conditions using AND or OR logic.

Usage

combine_filters(..., op = "and", apply_when = NULL, debug = FALSE)

Arguments

Value

Combined binary selection matrix

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, 12)
rsi <- calc_rsi(sample_prices_weekly, 14)
# Create individual filters
high_momentum <- filter_above(momentum, 0.05)
moderate_rsi <- filter_between(rsi, 40, 60)
# Combine them
combined <- combine_filters(high_momentum, moderate_rsi, op = "and")

Combine multiple score panels (mean/weighted/rank-average/trimmed)

Description

Combine multiple score panels (mean/weighted/rank-average/trimmed)

Usage

combine_scores(
  scores_list,
  method = c("mean", "weighted", "rank_avg", "trimmed_mean"),
  weights = NULL,
  trim = 0.1
)

Arguments

Combine Multiple Weighting Schemes

Description

Blends multiple weight matrices with specified weights. Useful for multi-factor strategies that combine different allocation approaches. Optimized using matrix operations for 1000x+ speedup.

Usage

combine_weights(weight_matrices, weights = NULL)

Arguments

Value

Data.table with blended portfolio weights

Examples

data("sample_prices_weekly")
# Calculate signals
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, n = 10)
volatility <- calc_rolling_volatility(sample_prices_weekly, lookback = 20)

# Combine momentum and low-vol weights
mom_weights <- weight_by_signal(selected, momentum)
vol_weights <- weight_by_signal(selected, invert_signal(volatility))
combined <- combine_weights(list(mom_weights, vol_weights), weights = c(0.7, 0.3))

Convert Data to N-Week Frequency

Description

Resamples daily or weekly data to n-week periods. Handles week-ending calculations and various aggregation methods.

Usage

convert_to_nweeks(data, n = 1, method = "last")

Arguments

Value

Data.table resampled to n-week frequency

Examples

data("sample_prices_daily")
# Convert daily to weekly
weekly <- convert_to_nweeks(sample_prices_daily, n = 1)
# Convert to bi-weekly
biweekly <- convert_to_nweeks(sample_prices_daily, n = 2)

Count finite entries per date

Description

Count finite entries per date

Usage

coverage_by_date(panel)

Arguments

Value

data.table with ⁠Date, n_finite⁠.

Convert Continuous Indicator to Discrete Regimes

Description

Transforms continuous indicators into discrete regime categories.

Usage

create_regime_buckets(
  indicator,
  breakpoints,
  labels = NULL,
  use_percentiles = FALSE
)

Arguments

Value

Integer vector of regime classifications

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, 10)
# Create VIX-like indicator from volatility
vol <- calc_rolling_volatility(sample_prices_weekly, lookback = 20)
vix_proxy <- vol$SPY * 100  # Scale to VIX-like values
regimes <- create_regime_buckets(vix_proxy, c(15, 25))

Load Price Data from CSV File

Description

Reads stock price data from CSV files with flexible column naming. Automatically standardizes to library format.

Usage

csv_adapter(
  file_path,
  date_col = "Date",
  symbol_col = "Symbol",
  price_col = "Price",
  frequency = "daily",
  symbol_order = NULL
)

Arguments

Value

Data.table with Date column and price columns

Examples

# Create a temporary tidy CSV from included weekly sample data (offline, fast)
data("sample_prices_weekly")
PW <- as.data.frame(sample_prices_weekly)
syms <- setdiff(names(PW), "Date")[1:2]

stk  <- stack(PW[1:10, syms])
tidy <- data.frame(
  Date   = rep(PW$Date[1:10], times = length(syms)),
  Symbol = stk$ind,
  Price  = stk$values
)

tmp <- tempfile(fileext = ".csv")
write.csv(tidy, tmp, row.names = FALSE)
prices <- csv_adapter(tmp)
head(prices)
unlink(tmp)

Purged/embargoed K-fold CV for sequence models (inside IS window)

Description

Purged/embargoed K-fold CV for sequence models (inside IS window)

Usage

cv_tune_seq(
  features_list,
  labels,
  is_start,
  is_end,
  steps_grid = c(12L, 26L, 52L),
  horizon = 4L,
  fit_fn,
  predict_fn,
  k = 3L,
  purge = NULL,
  embargo = NULL,
  group = c("pooled"),
  max_train_samples = NULL,
  max_val_samples = NULL,
  na_action = c("omit", "zero"),
  metric = c("spearman", "rmse")
)

Arguments

Value

data.table with columns like steps, folds, and CV score.

Demo sector map for examples/tests

Description

Demo sector map for examples/tests

Usage

demo_sector_map(symbols, n_groups = 2L)

Arguments

Download S&P 500 Sector Mappings from Wikipedia

Description

Scrapes current S&P 500 constituent list with sector classifications from Wikipedia and returns as a data.table.

Usage

download_sp500_sectors()

Value

Data.table with columns: Symbol, Security, Sector, SubIndustry, Industry

Examples

sectors <- download_sp500_sectors()
head(sectors)

Ensure Data.Table Without Mutation

Description

Converts input to data.table if needed, always returning a copy to prevent accidental data mutation. Core safety function used throughout the library.

Usage

ensure_dt_copy(data)

Arguments

Value

Copy of data as data.table

Examples

data("sample_prices_weekly")
dt <- ensure_dt_copy(sample_prices_weekly)  # Safe to modify dt

Evaluate scores vs labels (IC and hit-rate)

Description

Evaluate scores vs labels (IC and hit-rate)

Usage

evaluate_scores(
  scores,
  labels,
  top_frac = 0.2,
  method = c("spearman", "pearson")
)

Arguments

Value

data.table with ⁠Date, IC, hit_rate⁠; ICIR on attr(result,"ICIR").

Filter Stocks Above Threshold

Description

Convenience function to select stocks with signal above a value.

Usage

filter_above(signal_df, value)

Arguments

Value

Binary selection matrix

Examples

data("sample_prices_weekly")
rsi <- calc_rsi(sample_prices_weekly, 14)
high_rsi <- filter_above(rsi, 70)

Filter Stocks Below Threshold

Description

Convenience function to select stocks with signal below a value.

Usage

filter_below(signal_df, value)

Arguments

Value

Binary selection matrix

Examples

data("sample_prices_weekly")
rsi <- calc_rsi(sample_prices_weekly, 14)
oversold <- filter_below(rsi, 30)

Filter Stocks Between Two Values

Description

Selects stocks with signal values between lower and upper bounds.

Usage

filter_between(signal_df, lower, upper)

Arguments

Value

Binary selection matrix

Examples

data("sample_prices_weekly")
rsi <- calc_rsi(sample_prices_weekly, 14)
# Select stocks with RSI between 30 and 70
neutral_rsi <- filter_between(rsi, 30, 70)

Filter by Percentile

Description

Select securities in the top or bottom X percentile. More intuitive than filter_top_n when universe size varies.

Usage

filter_by_percentile(signal_df, percentile, type = c("top", "bottom"))

Arguments

Value

Binary selection matrix

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, 12)
# Select top 20th percentile
top_20pct <- filter_by_percentile(momentum, 20, type = "top")

Select Top or Bottom N Stocks by Signal

Description

Selects the top N (best) or worst N stocks based on signal strength. Optimized using matrix operations for 5-10x speedup.

Usage

filter_rank(signal_df, n, type = c("top", "worst"))

Arguments

Value

Binary selection matrix (1 = selected, 0 = not selected)

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, 12)
# Select 10 highest momentum stocks
top10 <- filter_rank(momentum, 10, type = "top")

Filter by Threshold Value

Description

Selects stocks above or below a threshold value.

Usage

filter_threshold(signal_df, value, type = c("above", "below"))

Arguments

Value

Binary selection matrix

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, 12)
# Select stocks with positive momentum
positive <- filter_threshold(momentum, 0, type = "above")

Select Top N Stocks by Signal Value

Description

Most commonly used filter function. Selects top N (highest) or bottom N (lowest) stocks by signal value. Optimized for 5-10x faster performance.

Usage

filter_top_n(signal_df, n, ascending = FALSE)

Arguments

Value

Binary selection matrix (1 = selected, 0 = not selected)

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, 12)
# Select 10 highest momentum stocks
top_momentum <- filter_top_n(momentum, n = 10)

Select Top N from Qualified Stocks

Description

Selects top N stocks by signal, but only from those meeting a condition. Combines qualification and ranking in one step.

Usage

filter_top_n_where(
  signal_df,
  n,
  condition_df,
  min_qualified = 1,
  ascending = FALSE
)

Arguments

Value

Binary selection matrix

Examples

data("sample_prices_weekly")
# Calculate indicators
momentum <- calc_momentum(sample_prices_weekly, 12)
ma20 <- calc_moving_average(sample_prices_weekly, 20)
distance_from_ma <- calc_distance(sample_prices_weekly, ma20)

# Top 10 momentum stocks from those above MA
above_ma <- filter_above(distance_from_ma, 0)
top_qualified <- filter_top_n_where(momentum, 10, above_ma)

Detect Data Frequency from Dates

Description

Automatically detects whether data is daily, weekly, monthly, or quarterly based on date spacing.

Usage

get_data_frequency(dates)

Arguments

Value

Character string: "daily", "weekly", "monthly", or "quarterly"

Examples

data("sample_prices_weekly")
freq <- get_data_frequency(sample_prices_weekly$Date)

Information Coefficient time series

Description

Information Coefficient time series

Usage

ic_series(scores, labels, method = c("spearman", "pearson"))

Arguments

Value

data.table with Date and IC.

Invert Signal Values for Preference Reversal

Description

Transforms signal values using (1 - value) to reverse preference direction. Useful when high values indicate something to avoid. For example, inverting volatility makes low-vol stocks appear as high signals.

Usage

invert_signal(signal_df)

Arguments

Value

Data frame with inverted signal values

Examples

data("sample_prices_weekly")
# Prefer low volatility stocks
volatility <- calc_rolling_volatility(sample_prices_weekly, 20)
stability_signal <- invert_signal(volatility)
# Select top 10 momentum stocks first
momentum <- calc_momentum(sample_prices_weekly, 12)
selected <- filter_top_n(momentum, 10)
# Weight by inverted volatility (low vol = high weight)
weights <- weight_by_signal(selected, stability_signal)

Join multiple panels on intersecting dates (unique symbol names)

Description

Join multiple panels on intersecting dates (unique symbol names)

Usage

join_panels(panels)

Arguments

Limit the number of positions in a selection matrix

Description

This function enforces position limits, keeping only the top N securities when more are selected.

Usage

limit_positions(
  selection_df,
  max_positions,
  ranking_signal = NULL,
  verbose = FALSE
)

Arguments

Value

Selection matrix with at most max_positions securities selected per period

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, 12)
# Create a selection of top 30 stocks
my_selections <- filter_top_n(momentum, 30)
# Limit to 20 positions, ranked by momentum
concentrated <- limit_positions(my_selections, 20, momentum)
# Limit to 10 positions, keeping existing selections randomly
limited <- limit_positions(my_selections, 10)

List available example scripts

Description

Shows all example scripts included with the PortfolioTesteR package. These examples demonstrate various strategy patterns and library functions.

Usage

list_examples()

Value

Character vector of example filenames

Examples

# See available examples
list_examples()

# Run a specific example
# run_example("example_momentum_basic.R")

Load Mixed Symbols Including VIX

Description

Handles loading regular stocks and VIX together, with VIX loaded separately without auto-update to avoid issues.

Usage

load_mixed_symbols(
  db_path,
  symbols,
  start_date,
  end_date,
  frequency = "weekly",
  use_adjusted = TRUE
)

Arguments

Value

data.table with all symbols properly loaded

Examples

mixed <- load_mixed_symbols(
  db_path  = "sp500.db",
  symbols  = c("AAPL", "MSFT", "VIX"),
  start_date = "2020-01-01",
  end_date   = "2020-12-31",
  frequency  = "weekly"
)
head(mixed)

Make future-return labels aligned to decision date

Description

Make future-return labels aligned to decision date

Usage

make_labels(prices, horizon = 4L, type = c("log", "simple", "sign"))

Arguments

Adapter for User-Provided Data

Description

Simple adapter for when users provide their own data frame. Ensures proper Date formatting and sorting.

Usage

manual_adapter(data, date_col = "Date")

Arguments

Value

Standardized data.table

Examples

# Use your own data frame
data("sample_prices_weekly")
my_prices <- manual_adapter(sample_prices_weekly)

Membership stability across dates

Description

Membership stability across dates

Usage

membership_stability(mask)

Arguments

Value

data.table with ⁠Date, stability⁠ (Jaccard vs previous date).

Calculate Sharpe Ratio with Frequency Detection

Description

Calculate Sharpe Ratio with Frequency Detection

Usage

metric_sharpe(bt)

Arguments

Value

Annualized Sharpe ratio

One-call backtest wrapper (tabular features)

Description

One-call backtest wrapper (tabular features)

Usage

ml_backtest(
  features_list,
  labels,
  fit_fn,
  predict_fn,
  schedule = list(is = 156L, oos = 4L, step = 4L),
  group = c("pooled", "per_symbol", "per_group"),
  transform = c("none", "zscore", "rank"),
  selection = list(top_k = 15L, max_per_group = NULL),
  group_map = NULL,
  weighting = list(method = "softmax", temperature = 12, floor = 0),
  caps = list(max_per_symbol = 0.1, max_per_group = NULL),
  turnover = NULL,
  prices,
  initial_capital = 1e+05,
  name = "ML backtest"
)

Arguments

Value

list: scores, mask, weights, backtest.

Examples

data(sample_prices_weekly); data(sample_prices_daily)
mom <- panel_lag(calc_momentum(sample_prices_weekly, 12), 1)
vol <- panel_lag(align_to_timeframe(
  calc_rolling_volatility(sample_prices_daily, 20),
  sample_prices_weekly$Date, "forward_fill"), 1)
Y <- make_labels(sample_prices_weekly, 4, "log")
fit_lm  <- function(X,y){ Xc <- cbind(1,X); list(coef=stats::lm.fit(Xc,y)$coefficients) }
pred_lm <- function(m,X){ as.numeric(cbind(1,X) %*% m$coef) }
res <- ml_backtest(list(mom=mom, vol=vol), Y, fit_lm, pred_lm,
                   schedule = list(is=52,oos=4,step=4),
                   transform = "zscore",
                   selection = list(top_k=10),
                   weighting = list(method="softmax", temperature=12),
                   caps = list(max_per_symbol=0.10),
                   prices = sample_prices_weekly, initial_capital = 1e5)
res$backtest

One-call backtest wrapper (sequence features)

Description

One-call backtest wrapper (sequence features)

Usage

ml_backtest_seq(
  features_list,
  labels,
  steps = 26L,
  horizon = 4L,
  fit_fn,
  predict_fn,
  schedule = list(is = 156L, oos = 4L, step = 4L),
  group = c("pooled", "per_symbol", "per_group"),
  group_map = NULL,
  normalize = c("none", "zscore", "minmax"),
  selection = list(top_k = 15L, max_per_group = NULL),
  weighting = list(method = "softmax", temperature = 12, floor = 0),
  caps = list(max_per_symbol = 0.1, max_per_group = NULL),
  prices,
  initial_capital = 1e+05,
  name = "SEQ backtest"
)

Arguments

Value

list: scores, mask, weights, backtest.

Examples

# as above, but with steps/horizon and normalize

Lag all symbol columns by k

Description

Lag all symbol columns by k

Usage

panel_lag(df, k = 1L)

Arguments

Plot Backtest Results

Description

S3 plot method for visualizing backtest performance.

Usage

## S3 method for class 'backtest_result'
plot(x, type = "performance", ...)

Arguments

Value

NULL (creates plot)

Examples

data("sample_prices_weekly")
mom <- calc_momentum(sample_prices_weekly, lookback = 12)
sel <- filter_top_n(mom, n = 10)
W   <- weight_equally(sel)
res <- run_backtest(sample_prices_weekly, W)
if (interactive()) plot(res, type = "performance")

Plot Parameter Grid Results (1D/2D/3D and Facets)

Description

Generic plotter for objects returned by run_param_grid(). Supported types:

• "line": 1D metric vs one parameter.

• "heatmap": 2D heatmap over two parameters.

• "surface": 3D surface (persp) over two parameters.

• "slices": 2D heatmaps faceted by a third parameter.

• "surface_slices": 3D surfaces faceted by a third parameter.

• "auto": chosen from the above based on the number of parameters.

Usage

## S3 method for class 'param_grid_result'
plot(
  x,
  y = NULL,
  type = c("auto", "line", "heatmap", "surface", "slices", "surface_slices"),
  metric = NULL,
  params = NULL,
  fixed = list(),
  agg = c("mean", "median", "max"),
  na.rm = TRUE,
  palette = NULL,
  zlim = NULL,
  clip = c(0.02, 0.98),
  main = NULL,
  sub = NULL,
  xlab = NULL,
  ylab = NULL,
  ...
)

Arguments

Value

An invisible list describing the plot (kind/params/zlim/etc.).

See Also

run_param_grid(), print.param_grid_result()

Examples

  data(sample_prices_weekly)
  b <- function(prices, params, ...) {
    weight_equally(filter_top_n(calc_momentum(prices, params$lookback), 10))
  }
  opt <- run_param_grid(
    prices  = sample_prices_weekly,
    grid    = list(lookback = c(8, 12, 26)),
    builder = b
  )
  plot(opt, type = "line", params = "lookback")

Plot Performance Analysis Results

Description

S3 method for visualizing performance metrics. Supports multiple plot types including summary dashboard, return distributions, risk evolution, and rolling statistics.

Usage

## S3 method for class 'performance_analysis'
plot(x, type = "summary", ...)

Arguments

Value

NULL (creates plot)

Examples

data("sample_prices_weekly")
data("sample_prices_daily")
syms_all <- intersect(names(sample_prices_weekly)[-1], names(sample_prices_daily)[-1])
syms <- syms_all[seq_len(min(3L, length(syms_all)))]
P <- sample_prices_weekly[, c("Date", syms), with = FALSE]
D <- sample_prices_daily[,  c("Date", syms), with = FALSE]
mom <- calc_momentum(P, lookback = 12)
sel <- filter_top_n(mom, n = 3)
W   <- weight_equally(sel)
res <- run_backtest(P, W)
perf <- analyze_performance(res, D, benchmark_symbol = syms[1])
if (interactive()) {
  plot(perf, type = "summary")
}

Plot Walk-Forward Results

Description

Visual diagnostics for a wf_optimization_result returned by run_walk_forward(). Supported types:

• "parameters": best parameter values chosen per window.

• "is_oos": in-sample vs out-of-sample scores by window.

• "equity": stitched out-of-sample equity curve.

• "drawdown": drawdown of the stitched OOS curve.

• "windows": per-window bar/line chart of an OOS metric (see metric).

• "stability": summary of parameter stability.

• "distributions": distributions of IS/OOS scores across windows.

Usage

## S3 method for class 'wf_optimization_result'
plot(
  x,
  y = NULL,
  type = c("parameters", "is_oos", "equity", "drawdown", "windows", "stability",
    "distributions"),
  param = NULL,
  metric = NULL,
  main = NULL,
  sub = NULL,
  xlab = NULL,
  ylab = NULL,
  ...
)

Arguments

Value

Invisibly, a small list describing the plot.

See Also

run_walk_forward(), wf_report(), print.wf_optimization_result()

Examples

  data(sample_prices_weekly)
  b <- function(prices, params, ...) {
    weight_equally(filter_top_n(calc_momentum(prices, params$lookback),
                                params$top_n))
  }
  wf <- run_walk_forward(
    prices = sample_prices_weekly,
    grid   = list(lookback = c(8, 12, 26), top_n = c(5, 10)),
    builder = b,
    is_periods = 52, oos_periods = 26, step = 26
  )
  plot(wf, type = "parameters")
  plot(wf, type = "is_oos", metric = "OOS_Score")

Print Backtest Results

Description

S3 print method for backtest results. Shows key performance metrics.

Usage

## S3 method for class 'backtest_result'
print(x, ...)

Arguments

Value

Invisible copy of x

Examples

data("sample_prices_weekly")
mom <- calc_momentum(sample_prices_weekly, lookback = 12)
sel <- filter_top_n(mom, n = 10)
W   <- weight_equally(sel)
res <- run_backtest(sample_prices_weekly, W)
print(res)

Print a param_grid_result

Description

Print a param_grid_result

Usage

## S3 method for class 'param_grid_result'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print Performance Analysis Results

Description

S3 method for printing performance analysis with key metrics including risk-adjusted returns, drawdown statistics, and benchmark comparison.

Usage

## S3 method for class 'performance_analysis'
print(x, ...)

Arguments

Value

Invisible copy of x

Examples

data("sample_prices_weekly")
data("sample_prices_daily")
syms_all <- intersect(names(sample_prices_weekly)[-1], names(sample_prices_daily)[-1])
syms <- syms_all[seq_len(min(3L, length(syms_all)))]
P <- sample_prices_weekly[, c("Date", syms), with = FALSE]
D <- sample_prices_daily[,  c("Date", syms), with = FALSE]
mom <- calc_momentum(P, lookback = 12)
sel <- filter_top_n(mom, n = 3)
W   <- weight_equally(sel)
res <- run_backtest(P, W)
perf <- analyze_performance(res, D, benchmark_symbol = syms[1])
print(perf)  # or just: perf

Print a wf_optimization_result

Description

Print a wf_optimization_result

Usage

## S3 method for class 'wf_optimization_result'
print(x, ...)

Arguments

Value

Invisibly returns x.

Rank Indicators Within Each Sector

Description

Ranks stocks within their sector for sector-neutral strategies. Enables selecting best stocks from each sector regardless of sector performance. Optimized using matrix operations within groups.

Usage

rank_within_sector(
  indicator_df,
  sector_mapping,
  method = c("percentile", "rank", "z-score"),
  min_sector_size = 3
)

Arguments

Value

Data frame with within-sector ranks/scores

Examples

data("sample_prices_weekly")
data("sample_sp500_sectors")
momentum <- calc_momentum(sample_prices_weekly, 12)
sector_ranks <- rank_within_sector(momentum, sample_sp500_sectors)

Rebalance calendar (rows with non-zero allocation)

Description

Rebalance calendar (rows with non-zero allocation)

Usage

rebalance_calendar(weights)

Arguments

Value

data.table with ⁠Date, row⁠ indices where a rebalance occurred.

Optimized recursive bisection for HRP

Description

Optimized recursive bisection for HRP

Optimized recursive bisection for HRP

Usage

recursive_bisection_optimized(cov_matrix, asset_order)

recursive_bisection_optimized(cov_matrix, asset_order)

Rolling fit/predict for tabular features (pooled / per-symbol / per-group)

Description

Rolling fit/predict for tabular features (pooled / per-symbol / per-group)

Usage

roll_fit_predict(
  features_list,
  label,
  fit_fn,
  predict_fn,
  is_periods = 156L,
  oos_periods = 4L,
  step = 4L,
  group = c("pooled", "per_symbol", "per_group"),
  group_map = NULL,
  na_action = c("omit", "zero")
)

Arguments

Value

wide panel of scores: Date + symbols.

Examples

data(sample_prices_weekly); data(sample_prices_daily)
mom <- panel_lag(calc_momentum(sample_prices_weekly, 12), 1)
vol <- panel_lag(align_to_timeframe(
  calc_rolling_volatility(sample_prices_daily, 20),
  sample_prices_weekly$Date, "forward_fill"), 1)
Y <- make_labels(sample_prices_weekly, horizon = 4, type = "log")
fit_lm  <- function(X,y){ Xc <- cbind(1,X); list(coef=stats::lm.fit(Xc,y)$coefficients) }
pred_lm <- function(m,X){ as.numeric(cbind(1,X) %*% m$coef) }
S <- roll_fit_predict(list(mom=mom, vol=vol), Y, fit_lm, pred_lm, 52, 4, 4)
head(S)

Rolling fit/predict for sequence models (flattened steps-by-p features)

Description

Rolling fit/predict for sequence models (flattened steps-by-p features)

Usage

roll_fit_predict_seq(
  features_list,
  labels,
  steps = 26L,
  horizon = 4L,
  fit_fn,
  predict_fn,
  is_periods = 156L,
  oos_periods = 4L,
  step = 4L,
  group = c("pooled", "per_symbol", "per_group"),
  group_map = NULL,
  normalize = c("none", "zscore", "minmax"),
  min_train_samples = 50L,
  na_action = c("omit", "zero")
)

Arguments

Value

wide panel of scores.

Examples

data(sample_prices_weekly); data(sample_prices_daily)
mom <- panel_lag(calc_momentum(sample_prices_weekly, 12), 1)
vol <- panel_lag(align_to_timeframe(
  calc_rolling_volatility(sample_prices_daily, 20),
  sample_prices_weekly$Date, "forward_fill"), 1)
Y <- make_labels(sample_prices_weekly, horizon = 4, type = "log")
fit_lm  <- function(X,y){ Xc <- cbind(1,X); list(coef=stats::lm.fit(Xc,y)$coefficients) }
pred_lm <- function(m,X){ as.numeric(cbind(1,X) %*% m$coef) }
S <- roll_fit_predict_seq(list(mom=mom, vol=vol), Y,
                          steps = 26, horizon = 4,
                          fit_fn = fit_lm, predict_fn = pred_lm,
                          is_periods = 104, oos_periods = 4, step = 4)
head(S)

Rolling IC mean / sd / ICIR

Description

Rolling IC mean / sd / ICIR

Usage

roll_ic_stats(ic_dt, window = 26L)

Arguments

Run Portfolio Backtest

Description

Main backtesting engine that simulates portfolio performance over time. Handles position tracking, transaction costs, and performance calculation.

Usage

run_backtest(
  prices,
  weights,
  initial_capital = 1e+05,
  name = "Strategy",
  verbose = FALSE,
  stop_loss = NULL,
  stop_monitoring_prices = NULL
)

Arguments

Value

backtest_result object with performance metrics

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, n = 10)
weights <- weight_equally(selected)
result <- run_backtest(sample_prices_weekly, weights)

Run an Example Script

Description

Executes an example script bundled in the package ⁠inst/examples/⁠ folder.

Usage

run_example(example_name, echo = TRUE)

Arguments

Value

Invisibly returns NULL. Runs the example for its side effects.

Examples

# Example (requires a real file under inst/examples):
# run_example("basic.R")

Run Parameter Grid Optimization (safe + ergonomic)

Description

Run Parameter Grid Optimization (safe + ergonomic)

Usage

run_param_grid(
  prices,
  grid,
  builder,
  metric = NULL,
  name_prefix = "Strategy",
  verbose = FALSE,
  light_mode = TRUE,
  precompute_returns = TRUE,
  builder_args = list(),
  n_cores = 1,
  fixed = NULL
)

Arguments

Value

param_grid_result

Walk-Forward Optimization Analysis

Description

Runs rolling IS/OOS optimization, reselects params each window, and backtests OOS performance (optionally with warmup tails).

Usage

run_walk_forward(
  prices,
  grid,
  builder,
  metric = NULL,
  is_periods = 52,
  oos_periods = 13,
  step = NULL,
  warmup_periods = 0,
  verbose = FALSE,
  light_mode = TRUE,
  precompute_all = TRUE,
  builder_args = list(),
  n_cores = 1
)

Arguments

Value

An object of class wf_optimization_result.

Safe ANY Operation with NA Handling

Description

Performs any() operation that returns FALSE when all values are NA instead of NA.

Usage

safe_any(x, ...)

Arguments

Value

Logical value (never NA)

Safe Division with NA and Zero Handling

Description

Performs division with automatic handling of NA values, zeros, and infinity. Returns 0 for division by zero and NA cases.

Usage

safe_divide(numerator, denominator)

Arguments

Value

Numeric vector with safe division results

Examples

safe_divide(c(10, 0, NA, 5), c(2, 0, 5, NA))  # Returns c(5, 0, 0, 0)

Sample Daily Stock Prices

Description

Daily closing prices for 20 stocks from 2017-2019. Contains the same symbols as sample_prices_weekly but at daily frequency for more granular analysis and performance calculations.

Usage

data(sample_prices_daily)

Format

A data.table with 754 rows and 21 columns:

Date

Date object, trading date

AAPL

Apple Inc. adjusted closing price

AMZN

Amazon.com Inc. adjusted closing price

BA

Boeing Co. adjusted closing price

BAC

Bank of America Corp. adjusted closing price

...

Additional stock symbols with adjusted closing prices

Source

Yahoo Finance historical data, adjusted for splits and dividends

Examples

data(sample_prices_daily)
head(sample_prices_daily)
# Get date range
range(sample_prices_daily$Date)

Sample Weekly Stock Prices

Description

Weekly closing prices for 20 stocks from 2017-2019. Data includes major stocks from various sectors and is suitable for demonstrating backtesting and technical analysis functions.

Usage

data(sample_prices_weekly)

Format

A data.table with 158 rows and 21 columns:

Date

Date object, weekly closing date (typically Friday)

AAPL

Apple Inc. adjusted closing price

AMZN

Amazon.com Inc. adjusted closing price

BA

Boeing Co. adjusted closing price

BAC

Bank of America Corp. adjusted closing price

...

Additional stock symbols with adjusted closing prices

Source

Yahoo Finance historical data, adjusted for splits and dividends

Examples

data(sample_prices_weekly)
head(sample_prices_weekly)
# Calculate momentum
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)

S&P 500 Sector Mappings

Description

Sector classifications for the stock symbols in the sample datasets. Note: ETFs (SPY, QQQ, etc.) are not included as they represent indices or sectors themselves rather than individual companies.

Usage

data(sample_sp500_sectors)

Format

A data.table with 18 rows and 2 columns:

Symbol

Character, stock ticker symbol

Sector

Character, GICS sector classification

Source

S&P 500 constituent data

Examples

data(sample_sp500_sectors)
head(sample_sp500_sectors)
# Count stocks per sector
table(sample_sp500_sectors$Sector)

Select top-K scores per date

Description

Select top-K scores per date

Usage

select_top_k_scores(scores, k, ties = "first")

Arguments

Value

logical mask panel (Date + symbols) marking selected names.

Select top-K per date with group caps

Description

Select top-K per date with group caps

Usage

select_top_k_scores_by_group(scores, k, group_map, max_per_group)

Arguments

Load Price Data from SQL Database

Description

Loads stock price data from SQLite database with automatic frequency conversion.

Usage

sql_adapter(
  db_path,
  symbols,
  start_date = NULL,
  end_date = NULL,
  auto_update = TRUE,
  frequency = "daily"
)

Arguments

Value

data.table with Date column and one column per symbol

Examples

prices <- sql_adapter(
  db_path   = "sp500.db",
  symbols   = c("AAPL", "MSFT"),
  start_date = "2020-01-01",
  end_date   = "2020-12-31",
  frequency  = "weekly"
)
head(prices)

Load Adjusted Price Data from SQL Database

Description

Loads adjusted stock prices (for splits/dividends) from SQLite.

Usage

sql_adapter_adjusted(
  db_path,
  symbols,
  start_date = NULL,
  end_date = NULL,
  auto_update = FALSE,
  frequency = "daily",
  use_adjusted = TRUE
)

Arguments

Value

data.table with Date column and adjusted prices per symbol

Examples

prices <- sql_adapter_adjusted(
  db_path   = "sp500.db",
  symbols   = c("AAPL", "MSFT"),
  start_date = "2020-01-01",
  end_date   = "2020-12-31",
  frequency  = "monthly"
)
head(prices)

Standardize Data to Library Format

Description

Internal function that converts various data formats to standard wide format with Date column and symbol columns.

Usage

standardize_data_format(
  data,
  date_col = "date",
  symbol_col = "symbol",
  price_col = "price",
  symbol_order = NULL
)

Arguments

Value

Standardized data.table

Summary method for backtest results

Description

Summary method for backtest results

Usage

## S3 method for class 'backtest_result'
summary(object, ...)

Arguments

Value

Invisible copy of the object

Switch Between Weighting Schemes

Description

Dynamically switches between two weighting schemes based on a signal. Enables tactical allocation changes.

Usage

switch_weights(weights_a, weights_b, use_b_condition, partial_blend = 1)

Arguments

Value

Combined weight matrix

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, n = 10)
weights_equal <- weight_equally(selected)
weights_signal <- weight_by_signal(selected, momentum)

# Create switching signal (example: use SPY momentum as regime indicator)
spy_momentum <- momentum$SPY
switch_signal <- as.numeric(spy_momentum > median(spy_momentum, na.rm = TRUE))
switch_signal[is.na(switch_signal)] <- 0

# Switch between strategies
final_weights <- switch_weights(weights_equal, weights_signal, switch_signal)

Per-date score transform (z-score or rank)

Description

Per-date score transform (z-score or rank)

Usage

transform_scores(
  scores,
  method = c("zscore", "rank"),
  per_date = TRUE,
  robust = FALSE
)

Arguments

Value

panel of transformed scores.

Quick grid tuning for tabular pipeline

Description

Quick grid tuning for tabular pipeline

Usage

tune_ml_backtest(
  features_list,
  labels,
  prices,
  fit_fn,
  predict_fn,
  schedule = list(is = 104L, oos = 4L, step = 4L),
  grid = list(top_k = c(10L, 15L), temperature = c(8, 12), method = c("softmax", "rank"),
    transform = c("zscore")),
  group = "pooled",
  selection_defaults = list(top_k = 15L, max_per_group = NULL),
  weighting_defaults = list(method = "softmax", temperature = 12, floor = 0),
  caps = list(max_per_symbol = 0.08),
  group_map = NULL,
  cost_bps = 0,
  freq = 52
)

Arguments

Value

data.table with metrics per grid row.

Turnover by date

Description

Turnover by date

Usage

turnover_by_date(weights)

Arguments

Value

data.table with ⁠Date, turnover⁠ (0.5 * L1 change).

Update VIX data in database

Description

Update VIX data in database

Usage

update_vix_in_db(db_path, from_date = NULL)

Arguments

Value

Number of rows updated (invisible)

Validate Data Format for Library Functions

Description

Checks that data meets library requirements: proper Date column, at least one symbol, correct data types. Prints diagnostic info.

Usage

validate_data_format(data)

Arguments

Value

TRUE if valid, stops with error if not

Examples

data("sample_prices_weekly")
# Check if data is properly formatted
validate_data_format(sample_prices_weekly)

Validate or synthesize a group map

Description

Validate or synthesize a group map

Usage

validate_group_map(symbols, group_map)

Arguments

Quick leakage guard: date alignment & NA expectations

Description

Quick leakage guard: date alignment & NA expectations

Usage

validate_no_leakage(features, labels, verbose = TRUE)

Arguments

Value

TRUE/FALSE (invisible), with messages if verbose = TRUE.

Validate Performance Analysis Inputs

Description

data("sample_prices_weekly") Checks that backtest result and daily prices are properly formatted with matching symbols and appropriate date coverage.

Usage

validate_performance_inputs(backtest_result, daily_prices, benchmark_symbol)

Arguments

Value

TRUE if valid, stops with error if not

Hierarchical Risk Parity Weighting

Description

Calculates portfolio weights using Hierarchical Risk Parity (HRP) methodology. HRP combines hierarchical clustering with risk-based allocation to create diversified portfolios that don't rely on unstable correlation matrix inversions.

Usage

weight_by_hrp(
  selected_df,
  prices_df,
  lookback_periods = 252,
  cluster_method = "ward.D2",
  distance_method = "euclidean",
  min_periods = 60,
  use_correlation = FALSE
)

Arguments

Details

The HRP algorithm:

1. Calculate returns from input prices

2. Compute covariance matrix from returns

3. Cluster assets based on distance matrix

4. Apply recursive bisection with inverse variance weighting

5. Results in naturally diversified portfolio without matrix inversion

The function accepts price data and calculates returns internally, matching the pattern of other library functions like calc_momentum().

Value

Weight matrix with same dates as selected_df

Examples

data("sample_prices_daily")
data("sample_prices_weekly")
# Create a selection first
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, n = 10)

# Using daily prices for risk calculation
weights <- weight_by_hrp(selected, sample_prices_daily, lookback_periods = 252)

# Using correlation-based clustering
weights <- weight_by_hrp(selected, sample_prices_daily, use_correlation = TRUE)

Rank-Based Portfolio Weighting

Description

Weights securities based on their rank rather than raw signal values. Useful when signal magnitudes are unreliable but ordering is meaningful.

Usage

weight_by_rank(
  selected_df,
  signal_df,
  method = c("linear", "exponential"),
  ascending = FALSE
)

Arguments

Value

Data.table with rank-based weights

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, 10)
# Linear rank weighting (best gets most)
weights <- weight_by_rank(selected, momentum, method = "linear")
# Exponential (heavy on top stocks)
weights_exp <- weight_by_rank(selected, momentum, method = "exponential")

Regime-Based Adaptive Weighting

Description

Applies different weighting methods based on market regime classification. Enables adaptive strategies that change allocation approach in different market conditions.

Usage

weight_by_regime(
  selected_df,
  regime,
  weighting_configs,
  signal_df = NULL,
  vol_timeframe_data = NULL,
  strategy_timeframe_data = NULL
)

Arguments

Value

Data.table with regime-adaptive weights

Examples

data("sample_prices_weekly")
# Create selection and signals
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, n = 10)

# Create a simple regime (example: based on market trend)
ma20 <- calc_moving_average(sample_prices_weekly, 20)
spy_price <- sample_prices_weekly$SPY
spy_ma <- ma20$SPY
regime <- ifelse(spy_price > spy_ma, 1, 2)

# Different weights for bull/bear markets
weighting_configs <- list(
  "1" = list(method = "equal"),
  "2" = list(method = "signal")
)
weights <- weight_by_regime(selected, regime, weighting_configs,
                            signal_df = momentum)

Risk Parity Weighting Suite

Description

Collection of risk-based weighting methods for portfolio construction. Each method allocates capital based on risk characteristics rather than market capitalization or arbitrary equal weights.

Usage

weight_by_risk_parity(
  selected_df,
  prices_df,
  method = c("inverse_vol", "equal_risk", "max_div"),
  lookback_periods = 252,
  min_periods = 60
)

Arguments

Details

Methods:

• inverse_vol: Weight inversely to volatility (1/'). Lower volatility stocks receive higher weights. Simple but effective.

• equal_risk: Equal Risk Contribution (ERC). Each position contributes equally to total portfolio risk. Uses iterative optimization.

• max_div: Maximum Diversification Portfolio. Maximizes the ratio of weighted average volatility to portfolio volatility.

The function accepts price data and calculates returns internally, ensuring consistency with other library functions. Daily prices are recommended for accurate volatility estimation.

Value

Weight matrix with same dates as selected_df, rows sum to 1

Examples

data("sample_prices_daily")
data("sample_prices_weekly")
# Create a selection first
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, n = 10)

# Simple inverse volatility weighting
weights <- weight_by_risk_parity(selected, sample_prices_daily, method = "inverse_vol")

# Equal Risk Contribution for balanced exposure
weights <- weight_by_risk_parity(selected, sample_prices_daily, method = "equal_risk")

# Maximum Diversification Portfolio
weights <- weight_by_risk_parity(selected, sample_prices_daily, method = "max_div")

Signal-Based Portfolio Weighting

Description

Weights selected securities proportionally to their signal strength. Stronger signals receive higher allocations.

Usage

weight_by_signal(selected_df, signal_df)

Arguments

Value

Data.table with signal-proportional weights

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, 10)
# Weight by momentum strength
weights <- weight_by_signal(selected, momentum)

Volatility-Based Portfolio Weighting

Description

Weights securities based on their volatility characteristics. Can prefer low-volatility (defensive) or high-volatility (aggressive) stocks.

Usage

weight_by_volatility(
  selected_df,
  vol_timeframe_data,
  strategy_timeframe_data = NULL,
  lookback_periods = 26,
  low_vol_preference = TRUE,
  vol_method = "std",
  weighting_method = c("rank", "equal", "inverse_variance")
)

Arguments

Value

Data.table with volatility-based weights

Examples

data("sample_prices_weekly")
data("sample_prices_daily")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, 10)
daily_vol <- calc_rolling_volatility(sample_prices_daily, lookback = 252)
aligned_vol <- align_to_timeframe(daily_vol, sample_prices_weekly$Date)
weights <- weight_by_volatility(selected, aligned_vol, low_vol_preference = TRUE)

Equal Weight Portfolio Construction

Description

Creates equal-weighted portfolio from selection matrix. The simplest and often most robust weighting scheme.

Usage

weight_equally(selected_df)

Arguments

Value

Data.table with equal weights for selected securities

Examples

data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, 10)
weights <- weight_equally(selected)

Map scores to portfolio weights

Description

Map scores to portfolio weights

Usage

weight_from_scores(
  scores,
  method = c("softmax", "rank", "linear", "equal"),
  temperature = 10,
  floor = 0
)

Arguments

Value

weight panel (Date + symbols) with non-negative rows summing to 1 over active dates.

Generate Walk-Forward Report

Description

Prints a concise summary of a wf_optimization_result: configuration, stitched OOS performance, and parameter stability.

Usage

wf_report(wf, digits = 4)

Arguments

Value

Invisibly returns the optimization summary data frame.

Stitch Out-of-Sample Results (overlap-safe)

Description

Concatenates OOS backtests and safely compounds returns on overlapping dates.

Usage

wf_stitch(oos_results, initial_value = 1e+05)

Arguments

Value

Data frame with columns: Date, Value.

Walk-forward sweep of tabular configs (window-wise distribution of metrics)

Description

Walk-forward sweep of tabular configs (window-wise distribution of metrics)

Usage

wf_sweep_tabular(
  features_list,
  labels,
  prices,
  fit_fn,
  predict_fn,
  schedule = list(is = 104L, oos = 4L, step = 4L),
  grid = list(top_k = c(10L, 15L), temperature = c(8, 12), method = c("softmax", "rank"),
    transform = c("zscore")),
  caps = list(max_per_symbol = 0.08, max_per_group = NULL),
  group_map = NULL,
  freq = 52,
  cost_bps = 0,
  max_windows = NULL,
  ic_method = "spearman"
)

Arguments

Value

data.table with medians/means across OOS windows.

Download Price Data from Yahoo Finance

Description

Downloads stock price data directly from Yahoo Finance using quantmod. No database required - perfect for quick analysis and experimentation. Get started with real data in under 5 minutes.

Usage

yahoo_adapter(symbols, start_date, end_date, frequency = "daily")

Arguments

Value

Data.table with Date column and one column per symbol

Examples

# Use included sample data
data(sample_prices_weekly)

# Build a quick momentum strategy with offline data
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, n = 2)
weights <- weight_equally(selected)
result <- run_backtest(sample_prices_weekly, weights, initial_capital = 100000)


# Download tech stocks (requires internet, skipped on CRAN)
if (requireNamespace("quantmod", quietly = TRUE)) {
  prices <- yahoo_adapter(
    symbols = c("AAPL", "MSFT", "GOOGL"),
    start_date = "2023-01-01",
    end_date = "2023-12-31",
    frequency = "weekly"
  )
  momentum <- calc_momentum(prices, lookback = 12)
}