inst/doc/getting-started.R

In PortfolioTesteR: Test Investment Strategies with English-Like Code

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  message = FALSE,
  warning = FALSE
)

## ----install, eval=FALSE------------------------------------------------------
# # Install from GitHub
# # (Skip during CRAN checks and vignette builds.)
# devtools::install_github("alb3rtazzo/PortfolioTesteR")

## ----load---------------------------------------------------------------------
library(PortfolioTesteR)

## ----strategy1----------------------------------------------------------------
# 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)

## ----strategy1_plot-----------------------------------------------------------
plot(result1, type = "performance")

## ----strategy2----------------------------------------------------------------
# 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)

## ----strategy2_plot-----------------------------------------------------------
plot(result2, type = "performance")

## ----strategy3----------------------------------------------------------------
# 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)

## ----strategy3_plot-----------------------------------------------------------
# Plot both separately to avoid cramped figures
plot(result3_with, type = "performance")
plot(result3_no, type = "performance")

## ----strategy4----------------------------------------------------------------
# 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)

## ----strategy4_plot-----------------------------------------------------------
plot(result4, type = "performance")

## ----strategy5----------------------------------------------------------------
# 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)

## ----strategy5_plot-----------------------------------------------------------
plot(result5, type = "performance")

## ----strategy6----------------------------------------------------------------
# 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)



## ----strategy6_plot-----------------------------------------------------------
plot(res_stochrsi, type = "performance")

## ----live_data, eval = identical(Sys.getenv("RUN_LIVE"), "true")--------------
# 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)
# 
# 

## ----help, eval=FALSE---------------------------------------------------------
# ?run_backtest
# ?calc_momentum
# ?filter_top_n
# ?analyze_performance