investlabr

Research Workflows for Macro and Cross-Asset Analysis

Reusable research workflows for macro-financial and cross-asset analysis. The package sits on top of data packages such as 'investdatar' and focuses on transforming existing market and macro data into analysis-ready research tables, factor and indicator constructions, event and regime overlays, sensitivity estimates, scenario comparisons, and research-grade visualization outputs. Portfolio risk-return comparisons use the mean-variance framework of Markowitz (1952) . Research-side volatility simulations include the generalized autoregressive conditional heteroskedasticity model of Bollerslev (1986) .

investlabr

Research workflows for macro and cross-asset market analysis

investlabr is an R package for macro-financial and cross-asset research workflows.

It sits on top of data packages such as investdatar and provides reusable tools for:

  • preparing research-ready datasets
  • constructing macro and market factors
  • analyzing events and regimes
  • estimating sensitivities and exposures
  • running scenario and structural-break simulations
  • producing research-grade charts, dashboards, and briefing outputs

In simple terms:

  • investdatar gets and stores the data
  • strategyr turns strategy logic into executable actions
  • investlabr thinks with the data and communicates the idea

Package Boundary

investlabr is not a raw data collection package and not a trading execution package.

Its role is the layer between:

  1. data availability
  2. research interpretation
  3. presentation-ready output

This package is being expanded from a narrower strategy-evaluation toolkit into a broader research workflow package. Some current functions still reflect the earlier scope and will be migrated into a clearer module structure over time.

investlabr is not the package that should decide live strategy actions. That role belongs more naturally to strategyr, where dynamic strategy logic and execution-oriented backtesting live. investlabr is the looser and more exploratory layer for visualizing market patterns, stress-testing ideas, and communicating investment thinking to a human audience.

In Scope

  • Research data preparation
  • Macro and cross-asset diagnostics
  • Event and regime analysis
  • Sensitivity and exposure analysis
  • Scenario and structural simulation
  • Visualization and briefing output

Examples of likely function families:

  • prep_*
  • factor_*
  • event_*
  • regime_*
  • sense_*
  • sim_*
  • viz_*
  • brief_*

Out of Scope

  • Raw data downloading, syncing, and source registry management
  • Agent memory, LLM orchestration, or workflow automation
  • Trade execution, broker integration, and stateful backtesting infrastructure
  • Portfolio accounting, holdings administration, and reconciliation

Those concerns belong in adjacent packages such as investdatar, dedicated orchestration wrappers, or trading-specific packages.

Relationship to strategyr

strategyr and investlabr are related, but they solve different problems.

  • strategyr is for timely investment decision-making
  • strategyr owns realistic, path-dependent, execution-oriented backtesting
  • strategyr should be able to support executable orders or action selection
  • investlabr is for exploratory research, brainstorming, visualization, and human-facing idea sharing
  • investlabr may keep lightweight or simplified backtesting when it helps explain a market pattern, but that is not its core identity

Relationship to investdatar

investdatar owns:

  • downloading
  • syncing
  • source metadata
  • local storage
  • source-specific parsing

investlabr owns:

  • preparation of analysis-ready research tables
  • factor and indicator construction
  • event and regime overlays
  • sensitivity estimation
  • simulation and scenario comparison
  • charting and briefing outputs

Current Status

The repository currently includes legacy functions centered on event evaluation, strategy performance review, portfolio analysis, and plotting. Those functions remain useful, but the package identity is now broader:

investlabr is an R package for reusable macro, rates, liquidity, and cross-asset research workflows, including factor construction, event/regime analysis, sensitivity estimation, scenario simulation, and research-grade visualization.

Installation

# install.packages("remotes")
remotes::install_github("OliverLDS/investlabr")

Requirements

  • R >= 4.1.0
  • Core imports: data.table, ggplot2, quadprog, Rcpp
  • Visualization helpers: ggrepel, patchwork, gridExtra, scales

Repository Docs

Current Module Layout

The codebase is now organized by module family:

  • prep-*: research data preparation scaffolds
  • factor-*: reusable factor and diagnostic builders
  • event-*: event-study style analytics
  • regime-*: regime definitions and comparisons scaffolds
  • sense-*: sensitivity and exposure scaffolds
  • sim-*: simulation and legacy backtesting helpers
  • viz-*: visualization and layout utilities
  • brief-*: briefing-oriented plots and summaries

Some current exported functions still use older eval_* and gen_* names. Those remain for compatibility while the package migrates toward the new module naming convention.

Visualization Styles

investlabr now includes a shared visualization style layer so charts can use a more professional and varied visual language without rewriting each plotting function.

Current named styles include:

  • research_note
  • macro_classic
  • terminal_risk
  • cross_asset_color
  • minimal_print
  • strategy_explain
  • presentation_bold
  • briefing_serif
  • institutional_blue
  • policy_memo
  • desk_monitor
  • client_slide
  • newswire_print

Current contexts include:

  • report
  • slide
  • dashboard

You can inspect or change the defaults with:

library(investlabr)

viz_style_get()
viz_style_set(style = "macro_classic", context = "slide")

Most plotting functions now accept style and context, for example:

gen_yield_curve_plot(curve_dt, style = "macro_classic", context = "slide")
gen_plot_event_tsline_cum_ret(evt, style = "presentation_bold", context = "report")
eval_strat_plot_tsline_eq(bt_res, style = "terminal_risk", context = "dashboard")

If INVESTLABR_CONFIG points to a YAML file with plot_compiler_name, viz_theme_apply() will append Compiled by: ... to plot captions by default. Higher-level wrappers such as gen_grid_of_plots_with_labels() and the patchwork-based market dashboard path also use the same compiler footer logic. You can suppress that per plot with show_compiler = FALSE.

If the compiler name contains special YAML characters such as @, :, #, or leading/trailing spaces, wrap the string in quotes.

plot_compiler_name: Oliver Zhou
plot_compiler_name: "@user_name"

Basic Usage

For larger real-data workflows and style/context showcases, see the gallery in inst/gallery/README.md. The README examples below stay relatively compact on purpose.

Event workflow

library(data.table)
library(investlabr)

DT <- data.table(
  datetime = seq.POSIXt(as.POSIXct("2024-01-01", tz = "UTC"), by = "day", length.out = 120),
  close = cumprod(1 + rnorm(120, mean = 0.001, sd = 0.01)),
  event_policy = as.integer(seq_len(120) %% 20 == 0)
)

evt <- eval_event_performance(DT, "event_policy", H = 1L:10L)
gen_plot_event_tsline_cum_ret(evt)

Event comparison workflow

library(data.table)
library(investlabr)

DT <- data.table(
  datetime = seq.POSIXt(as.POSIXct("2024-01-01", tz = "UTC"), by = "day", length.out = 150),
  close = cumprod(1 + rnorm(150, mean = 0.0005, sd = 0.01)),
  event_a = as.integer(seq_len(150) %% 25 == 0),
  event_b = as.integer(seq_len(150) %% 40 == 0)
)

evt_a <- eval_event_performance(DT, "event_a", H = 1L:15L)
evt_b <- eval_event_performance(DT, "event_b", H = 1L:15L)

gen_plot_comparing_events(
  evt_a, evt_b,
  first_event_label = "Event A",
  second_event_label = "Event B"
)

Yield-curve workflow

library(data.table)
library(investlabr)

yield_dt_list <- list(
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(5.1, 5.0)),
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(4.8, 4.7)),
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(4.5, 4.4)),
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(4.2, 4.1)),
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(4.0, 3.9)),
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(3.9, 3.8)),
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(3.8, 3.7)),
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(3.7, 3.6)),
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(3.6, 3.5)),
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(3.7, 3.6)),
  data.table(date = as.Date(c("2024-01-01", "2024-02-01")), value = c(3.8, 3.7))
)

curve_dt <- get_yield_data_DT(
  yield_dt_list,
  yield_dates = list(
    Now = as.Date("2024-02-01"),
    `One month ago` = as.Date("2024-01-01")
  )
)

gen_yield_curve_plot(curve_dt)

Exploratory backtesting workflow

This is the lightweight and communication-oriented backtesting layer that remains in investlabr. More realistic and execution-grade strategy logic should live in strategyr.

library(data.table)
library(investlabr)

DT <- data.table(
  datetime = seq.POSIXt(as.POSIXct("2024-01-01", tz = "UTC"), by = "day", length.out = 120),
  open = 100 + cumsum(rnorm(120, 0.1, 1)),
  close = 100 + cumsum(rnorm(120, 0.1, 1)),
  pos_demo = rep(c(0, 1, 1, 0, -1, -1), length.out = 120)
)

attr(DT, "inst_id") <- "DEMO-ASSET"
attr(DT$pos_demo, "strat_name") <- "demo_strategy"
attr(DT$pos_demo, "strat_par") <- list(window = 20)

bt_res <- eval_strat_performance(DT, "pos_demo")

eval_strat_plot_tsline_eq(bt_res)
eval_strat_plot_scatter_maxdd_annret(bt_res)

If you want multiple rows for the scatter plot, bind several backtest results first:

bt_res_list <- data.table::rbindlist(list(bt_res, bt_res))
eval_strat_plot_scatter_maxdd_annret(bt_res_list)

Portfolio mix workflow

library(data.table)
library(investlabr)

mk_bt_res <- function(asset_name, seed) {
  set.seed(seed)
  DT <- data.table(
    datetime = seq.POSIXt(as.POSIXct("2024-01-01", tz = "UTC"), by = "day", length.out = 120),
    open = 100 + cumsum(rnorm(120, 0.1, 1)),
    close = 100 + cumsum(rnorm(120, 0.1, 1)),
    pos_demo = rep(c(0, 1, 1, 0, -1, -1), length.out = 120)
  )
  attr(DT, "inst_id") <- asset_name
  attr(DT$pos_demo, "strat_name") <- "demo_strategy"
  attr(DT$pos_demo, "strat_par") <- list(window = 20)
  eval_strat_performance(DT, "pos_demo")
}

bt_a <- mk_bt_res("DEMO-ASSET-A", seed = 1)
bt_b <- mk_bt_res("DEMO-ASSET-B", seed = 2)
bt_res_list <- data.table::rbindlist(list(bt_a, bt_b), fill = TRUE)

# Keep the target feasible for the sample inputs.
target_return <- min(bt_res_list$annual_return) * 0.8

weight_res <- get_optimal_weights(bt_res_list, target_return = target_return)
port_res <- eval_portfolio_performance(bt_res_list, target_return = target_return)

weight_res$optimal_weights_table
eval_strat_plot_tsline_eq(port_res)

Market chart workflow

library(data.table)
library(investlabr)

set.seed(1)
n <- 40
datetime <- seq.POSIXt(as.POSIXct("2024-01-01", tz = "UTC"), by = "day", length.out = n)
open <- 100 + cumsum(rnorm(n, 0.1, 1))
close <- open + rnorm(n, 0, 1)
upper_wick <- abs(rnorm(n, 0.8, 0.3))
lower_wick <- abs(rnorm(n, 0.8, 0.3))

DT <- data.table(
  datetime = datetime,
  open = open,
  high = pmax(open, close) + upper_wick,
  low = pmin(open, close) - lower_wick,
  close = close
)

gen_candle_plots_with_sr_lines(
  DT,
  support_pts = c(95, 97.5),
  resistance_pts = c(108, 112)
)

Multi-panel layout workflow

library(data.table)
library(investlabr)

set.seed(1)
DT <- data.table(
  date = seq.Date(as.Date("2024-01-01"), by = "day", length.out = 150),
  close = 100 + cumsum(rnorm(150, 0.05, 1)),
  event_a = as.integer(seq_len(150) %% 25 == 0),
  event_b = as.integer(seq_len(150) %% 40 == 0)
)

p1 <- gen_plot_event_tsline_cum_ret(
  eval_event_performance(DT, "event_a", H = 1L:15L),
  show_compiler = FALSE
)
p2 <- gen_plot_event_tsline_cum_ret(
  eval_event_performance(DT, "event_b", H = 1L:15L),
  show_compiler = FALSE
)

gen_grid_of_plots_with_labels(
  plots = list(p1, p2),
  n_rows = 1,
  n_cols = 2,
  col_labs = c("Event A", "Event B"),
  title = "Event Comparison"
)

Multi-panel layout workflow with real data

This example uses investdatar and Yahoo Finance data to build a 2 by 2 cross-asset event board. Equity, bond, FX, and commodity each get one subplot, and the symbols within each asset class are overlaid with different colors. Each series is indexed to 100 on the last trading day on or before the event date so the post-event paths are directly comparable inside each panel.

library(data.table)
library(ggplot2)
library(investdatar)
library(investlabr)

event_date <- as.Date("2026-02-28")
event_label <- "Feb 28, 2026 (Iran War)"
from_date <- "2026-01-15"
to_date <- "2026-04-01"

groups <- list(
  Equity = c("^GSPC", "^STOXX50E", "^N225", "^HSI"),
  Bond = c("ZT=F", "HYG", "LQD"),
  FX = c("DX-Y.NYB", "EURUSD=X", "GBPUSD=X"),
  Commodity = c("GC=F", "HG=F", "CL=F")
)

all_symbols <- unlist(groups, use.names = FALSE)
invisible(lapply(
  all_symbols,
  function(sym) investdatar::sync_local_quantmod_OHLC(
    ticker = sym,
    label = sym,
    from = from_date,
    to = to_date,
    src = "yahoo"
  )
))

make_asset_panel <- function(symbols, panel_title) {
  dt_list <- lapply(symbols, function(symbol) {
    dt <- investdatar::get_local_quantmod_OHLC(symbol, src = "yahoo")
    dt <- data.table::as.data.table(dt)[date >= as.Date(from_date) & date <= as.Date(to_date)]
    event_ref_date <- max(dt$date[dt$date <= event_date])
    event_ref_close <- dt[date == event_ref_date, close][1]
    dt[, `:=`(
      symbol = symbol,
      event_ref_date = event_ref_date,
      index100 = close / event_ref_close * 100
    )]
    dt
  })

  panel_dt <- data.table::rbindlist(dt_list, fill = TRUE)
  subtitle_date <- panel_dt[, min(event_ref_date)]
  last_labels <- panel_dt[!is.na(index100), .SD[.N], by = symbol]

  p <- ggplot(panel_dt, aes(x = datetime, y = index100, color = symbol)) +
    geom_line(linewidth = 0.9) +
    geom_text(
      data = last_labels,
      aes(label = symbol),
      hjust = -0.1,
      vjust = 0.5,
      show.legend = FALSE,
      size = 3.2
    ) +
    labs(
      title = panel_title,
      subtitle = paste0("Indexed to 100 on ", format(subtitle_date, "%Y-%m-%d")),
      x = "",
      y = ""
    )

  p <- viz_theme_apply(
    p,
    style = "macro_classic",
    context = "report",
    legend_position = "bottom",
    show_compiler = FALSE
  )
  p <- viz_annotate_event_lines(
    p,
    data.table::data.table(datetime = as.POSIXct(event_date), label = event_label),
    x_col = "datetime",
    label_col = "label",
    style = "macro_classic",
    context = "report"
  )
  p
}

equity_plot <- make_asset_panel(groups$Equity, "Equity")
bond_plot <- make_asset_panel(groups$Bond, "Bond")
fx_plot <- make_asset_panel(groups$FX, "FX")
commodity_plot <- make_asset_panel(groups$Commodity, "Commodity")

gen_grid_of_plots_with_labels(
  plots = list(
    equity_plot, bond_plot,
    fx_plot, commodity_plot
  ),
  n_rows = 2,
  n_cols = 2,
  title = "Cross-Asset Performance After Feb 28, 2026",
  bottom = "Each series is indexed to 100 at the last trading date on or before the event date."
)

Working with investdatar

investlabr assumes data has already been retrieved or synchronized elsewhere. A typical workflow is:

# library(investdatar)
# library(investlabr)
#
# fred_local <- investdatar::get_local_FRED_data("DGS10")
# curve_inputs <- ...
# research_tbl <- investlabr::get_yield_data_DT(curve_inputs, yield_dates = ...)

End-To-End With investdatar

The example below shows the intended workflow boundary directly: investdatar reads the local FRED yield series, and investlabr converts those series into a research-ready yield-curve comparison chart.

library(investdatar)
library(investlabr)

series_ids <- c(
  "DGS1MO", "DGS3MO", "DGS6MO", "DGS1", "DGS2", "DGS3",
  "DGS5", "DGS7", "DGS10", "DGS20", "DGS30"
)

# Optional if you have not synced these series locally yet:
invisible(lapply(series_ids, investdatar::sync_local_fred_data))

yield_dt_list <- lapply(series_ids, investdatar::get_local_FRED_data)

curve_dt <- investlabr::get_yield_data_DT(
  yield_dt_list = yield_dt_list,
  yield_dates = list(
    Now = Sys.Date(),
    `One month ago` = Sys.Date() - 30,
    `One year ago` = Sys.Date() - 365
  )
)

investlabr::gen_yield_curve_plot(
  curve_dt,
  selected_windows = c("Now", "One month ago", "One year ago")
)

Yahoo Finance candles via investdatar

This example uses investdatar as the Yahoo Finance access layer and investlabr as the charting layer.

library(data.table)
library(investdatar)
library(investlabr)

investdatar::sync_local_quantmod_OHLC(
  ticker = "SPY",
  from = "2024-01-01",
  to = "2024-12-31",
  src = "yahoo"
)

spy_dt <- investdatar::get_local_quantmod_OHLC("SPY", src = "yahoo")

investlabr::gen_candle_plots_with_sr_lines(
  data.table::as.data.table(spy_dt)[1:60, .(datetime, open, high, low, close)],
  support_pts = c(500, 520),
  resistance_pts = c(560, 580)
)

These examples show the intended package boundary:

  • investdatar handles data syncing and local access
  • investlabr turns those series into reusable research objects and charts
  • strategyr is where dynamically adaptive, execution-grade strategy logic should live

Design Principles

  • Keep the package focused on research workflows, not data acquisition.
  • Prefer tidy, reusable transformation functions over one-off scripts.
  • Return objects that are easy to reuse: data.table, tidy tables, ggplot objects, or small named lists.
  • Separate data preparation, analytic logic, and visualization.
  • Avoid hard-coded file paths, agent objects, or personal local directories.
  • Make functions composable for dashboards and research notes.
  • Keep exploratory backtesting lightweight and communication-oriented; execution-grade strategy logic belongs in strategyr.

Testing

investlabr now includes a basic testthat setup for smoke coverage of package-level workflows.

# install.packages("testthat")
testthat::test_dir("tests/testthat")

License

MIT © 2025 Oliver Zhou

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.
install.packages("investlabr")

0.0.6.2 by Oliver Zhou, 11 hours ago

https://github.com/OliverLDS/investlabr

Report a bug at https://github.com/OliverLDS/investlabr/issues

Browse source code at https://github.com/cran/investlabr

Authors: Oliver Zhou [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports data.table, ggplot2, ggrepel, grid, gridExtra, jsonlite, patchwork, quadprog, rlang, scales, stats, utils, yaml, Rcpp

Suggests svglite, testthat

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