Getting Started with PortfolioTesteR"

In PortfolioTesteR: Test Investment Strategies with English-Like Code

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PortfolioTesteR: Test Investment Strategies with English-Like Code

PortfolioTesteR makes quantitative investing accessible through intuitive, English-like syntax. This vignette walks through five strategies from beginner to advanced, all using the included sample data so it builds quickly and reliably.

Note: Some functions also support external data (e.g., yahoo_adapter()), but for CRAN-friendly vignettes we use the bundled datasets.

Installation

# Install from GitHub
# (Skip during CRAN checks and vignette builds.)
devtools::install_github("alb3rtazzo/PortfolioTesteR")

Load the Library

library(PortfolioTesteR)

Strategy 1: Simple Momentum (Beginner)

Buy the stocks with the highest 12-week returns, weight them equally, and backtest.

# Load included weekly prices
data(sample_prices_weekly)

# 1) Momentum signal
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)

# 2) Select top 10 by momentum
selected <- filter_top_n(momentum, n = 10)

# 3) Equal weights
weights <- weight_equally(selected)

# 4) Backtest
result1 <- run_backtest(
  prices = sample_prices_weekly,
  weights = weights,
  initial_capital = 100000,
  name = "Simple Momentum"
)

# 5) Results
print(result1)
summary(result1)

plot(result1, type = "performance")

Strategy 2: Multi-Signal Combination (Intermediate)

Combine momentum (good = high) and stability (good = low volatility). Select stocks that rank well on both, then combine weights.

# Need daily data for volatility
data(sample_prices_daily)

# A) Momentum (12-week)
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)

# B) Daily volatility -> align weekly -> invert (low vol = high score)
daily_vol <- calc_rolling_volatility(sample_prices_daily, lookback = 20)
weekly_vol <- align_to_timeframe(
  high_freq_data = daily_vol,
  low_freq_dates = sample_prices_weekly$Date,
  method = "forward_fill"
)
stability_signal <- invert_signal(weekly_vol)

# Select top 20 for each signal
m_sel <- filter_top_n(momentum, n = 20)
s_sel <- filter_top_n(stability_signal, n = 20)

# AND-combine the selections
both <- combine_filters(m_sel, s_sel, op = "and")

# Weight each way then blend 60/40
w_mom <- weight_by_signal(both, momentum)
w_stab <- weight_by_signal(both, stability_signal)
weights2 <- combine_weights(list(w_mom, w_stab), weights = c(0.6, 0.4))

# Backtest
result2 <- run_backtest(
  prices = sample_prices_weekly,
  weights = weights2,
  initial_capital = 100000,
  name = "Momentum + Low Vol"
)

print(result2)
summary(result2)

plot(result2, type = "performance")

Strategy 3: Risk-Managed Momentum with Stop Loss (Intermediate)

Add a 15% stop loss monitored on daily prices. (We compare the same strategy with and without stops.)

# Signals and selection
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
sel <- filter_top_n(momentum, n = 10)
weights_mom <- weight_by_signal(sel, momentum)

# With 15% stop-loss (daily monitoring)
result3_with <- run_backtest(
  prices = sample_prices_weekly,
  weights = weights_mom,
  initial_capital = 100000,
  name = "Momentum with 15% Stop Loss",
  stop_loss = 0.15,
  stop_monitoring_prices = sample_prices_daily
)

# Without stop-loss
result3_no <- run_backtest(
  prices = sample_prices_weekly,
  weights = weights_mom,
  initial_capital = 100000,
  name = "Momentum without Stop Loss"
)

cat("WITH Stop Loss:\n")
print(result3_with)
cat("\nWITHOUT Stop Loss:\n")
print(result3_no)

# Plot both separately to avoid cramped figures
plot(result3_with, type = "performance")
plot(result3_no, type = "performance")

Strategy 4: Regime-Adaptive Volatility (Advanced)

Detect market volatility regimes using SPY's rolling volatility. Use defensive weights in high-vol regimes and aggressive weights in low-vol regimes.

# Extract SPY for regime detection
spy_prices <- sample_prices_weekly[, .(Date, SPY)]

# Trading universe (exclude SPY)
trading_symbols <- setdiff(names(sample_prices_weekly), c("Date", "SPY"))
trading_prices <- sample_prices_weekly[, c("Date", trading_symbols), with = FALSE]
trading_daily  <- sample_prices_daily[,  c("Date", trading_symbols), with = FALSE]

# SPY weekly returns & 20-week rolling volatility (annualized)
spy_returns <- c(NA, diff(spy_prices$SPY) / head(spy_prices$SPY, -1))
spy_vol <- zoo::rollapply(spy_returns, width = 20, FUN = sd, fill = NA, align = "right") * sqrt(52)

# High-vol regime = above median
vol_threshold <- median(spy_vol, na.rm = TRUE)
high_vol <- spy_vol > vol_threshold

# Selection by momentum
mom <- calc_momentum(trading_prices, lookback = 12)
sel <- filter_top_n(mom, n = 15)

# Defensive (prefer low vol) vs Aggressive (prefer high vol) weights
w_def <- weight_by_volatility(
  selected_df = sel,
  vol_timeframe_data = trading_daily,
  strategy_timeframe_data = trading_prices,
  lookback_periods = 20,
  low_vol_preference = TRUE,
  vol_method = "std"
)

w_agg <- weight_by_volatility(
  selected_df = sel,
  vol_timeframe_data = trading_daily,
  strategy_timeframe_data = trading_prices,
  lookback_periods = 20,
  low_vol_preference = FALSE,
  vol_method = "std"
)

# Switch weights by regime (defensive when high-vol is TRUE)
weights4 <- switch_weights(
  weights_a = w_agg,  # used when condition is FALSE (low vol)
  weights_b = w_def,  # used when condition is TRUE  (high vol)
  use_b_condition = high_vol
)

result4 <- run_backtest(
  prices = trading_prices,
  weights = weights4,
  initial_capital = 100000,
  name = "Regime-Adaptive Strategy"
)

print(result4)
summary(result4)

plot(result4, type = "performance")

Strategy 5: Multi-Factor with Position Limits (Advanced)

Combine momentum and stability signals, then enforce a max positions limit to control concentration. Weight 70% by momentum strength and 30% by stability.

# Signals
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
daily_vol <- calc_rolling_volatility(sample_prices_daily, lookback = 20)
weekly_vol <- align_to_timeframe(daily_vol, sample_prices_weekly$Date, method = "forward_fill")
stability <- invert_signal(weekly_vol)

# Selection & position cap
top30 <- filter_top_n(momentum, n = 30)
sel15 <- limit_positions(top30, momentum, max_positions = 15)

# Weights and blend (70/30)
w_m <- weight_by_signal(sel15, momentum)
w_s <- weight_by_signal(sel15, stability)
weights5 <- combine_weights(list(w_m, w_s), weights = c(0.7, 0.3))

# Backtest
result5 <- run_backtest(
  prices = sample_prices_weekly,
  weights = weights5,
  initial_capital = 100000,
  name = "Multi-Factor with Position Limits"
)

print(result5)
summary(result5)

plot(result5, type = "performance")

Strategy 6: StochRSI Acceleration + Inverse-Vol Risk Parity

Advanced Strategy: StochRSI Acceleration + Inverse-Vol Risk Parity - Gate to high StochRSI (>= 0.80), then select top-12 by acceleration - Allocate by inverse-volatility risk parity using DAILY prices - Backtest on the weekly grid (bundled datasets only; CRAN-friendly)

# Data
data(sample_prices_weekly)
data(sample_prices_daily)

# Exclude broad ETFs from stock-selection universe
symbols_all   <- setdiff(names(sample_prices_weekly), "Date")
stock_symbols <- setdiff(symbols_all, c("SPY", "TLT"))

weekly_stocks <- sample_prices_weekly[, c("Date", stock_symbols), with = FALSE]
daily_stocks  <- sample_prices_daily[,  c("Date", stock_symbols), with = FALSE]

# StochRSI "acceleration" signal (weekly)
stochrsi    <- calc_stochrsi(weekly_stocks, length = 14)   # in [0,1]
stochrsi_ma <- calc_moving_average(stochrsi, window = 5)
accel       <- calc_distance(stochrsi, stochrsi_ma)        # positive = rising

# Gate to high StochRSI zone, then take top-12 by acceleration
high_zone <- filter_above(stochrsi, value = 0.80)
sel <- filter_top_n_where(
  signal_df     = accel,
  n             = 12,
  condition_df  = high_zone,
  min_qualified = 8,
  ascending     = FALSE
)

# Allocation: inverse-volatility risk parity (DAILY prices)
w_ivol <- weight_by_risk_parity(
  selected_df      = sel,
  prices_df        = daily_stocks,
  method           = "inverse_vol",
  lookback_periods = 126,  # ~6 months
  min_periods      = 60
)

# Backtest on the weekly grid
res_stochrsi <- run_backtest(
  prices          = weekly_stocks,
  weights         = w_ivol,
  initial_capital = 100000,
  name            = "StochRSI Accel + InvVol RP"
)

print(res_stochrsi)
summary(res_stochrsi)

plot(res_stochrsi, type = "performance")

Optional: Live Data via Yahoo (CRAN-safe)

Below is a minimal example that fetches prices from Yahoo Finance and runs the same "calculate -> filter -> weight -> backtest" pipeline. The code is disabled inside CRAN/devtools checks. To run it locally, set Sys.setenv(RUN_LIVE = "true") before knitting.

library(PortfolioTesteR)

# Fetch weekly data for a small set of tickers
tickers <- c("AAPL","MSFT","AMZN","GOOGL","META")
px_weekly <- yahoo_adapter(
  symbols   = tickers,
  frequency = "weekly"
)

# Simple momentum: top-3 by 12-week return, equal weight
mom  <- calc_momentum(px_weekly, lookback = 12)
sel  <- filter_top_n(mom, n = 3)
w_eq <- weight_equally(sel)

res_yh <- run_backtest(
  prices          = px_weekly,
  weights         = w_eq,
  initial_capital = 100000,
  name            = "Yahoo: Simple Momentum (Top 3)"
)

print(res_yh)
summary(res_yh)

Key Concepts Recap

The PortfolioTesteR Pattern

1. Calculate signals (momentum, volatility, etc.)
2. Filter the universe (top-N, thresholds, logical combinations)
3. Weight the portfolio (equally, by signal, by volatility, etc.)
4. Backtest the strategy
5. Analyze with built-in metrics and visualizations

Function Families

• Indicators: calc_momentum(), calc_rsi(), calc_rolling_volatility(), calc_moving_average()
• Selection: filter_top_n(), filter_above(), filter_between(), combine_filters()
• Weighting: weight_equally(), weight_by_signal(), weight_by_rank(), weight_by_volatility(), combine_weights()
• Execution: run_backtest()
• Utilities: align_to_timeframe(), invert_signal(), limit_positions(), switch_weights()
• Analysis: analyze_performance(), summary(), plot()

Getting Help

?run_backtest
?calc_momentum
?filter_top_n
?analyze_performance

Citation

If you use PortfolioTesteR in your research, please cite:

Pallotta, A. (2025). PortfolioTesteR: Test Investment Strategies with English-Like Code. R package version 0.1.2. https://github.com/AlbertoPallotta/PortfolioTesteR

PortfolioTesteR documentation built on Nov. 5, 2025, 5:23 p.m.