strategyr

strategyr is an execution-oriented R package for modular strategy workflows. It is designed to transform already-available market features, portfolio state, and dynamic quantitative analysis into actionable signals, target positions, portfolio adjustments, and order intents.

The package emphasizes path-dependent backtesting so strategy logic is evaluated under evolving account state, execution assumptions, and market conditions rather than through purely static signal research.

Compared with investlabr, which is oriented toward exploratory research and communication, strategyr is intended for disciplined strategy logic and timely investment decision support.

Installation

devtools::install_github("OliverLDS/strategyr")

Usage Cases

1. Single-asset strategy backtest

library(data.table)
library(strategyr)

DT <- data.table(
  datetime = as.POSIXct("2020-01-01", tz = "UTC") + 86400 * 0:249
)
DT[, close := 100 + cumsum(sin(seq_len(.N) / 8) + 0.15)]
DT[, open := shift(close, fill = close[1])]
DT[, high := pmax(open, close) + 1]
DT[, low := pmin(open, close) - 1]

tgt_pos <- strat_donchian_turtle_tgt_pos(
  DT,
  entry_n = 30L,
  exit_n = 10L,
  target_size = 0.95
)

eq <- backtest_rcpp(
  timestamp = as.numeric(DT$datetime),
  open = DT$open,
  high = DT$high,
  low = DT$low,
  close = DT$close,
  tgt_pos = tgt_pos,
  pos_strat = rep(307L, nrow(DT)),
  tol_pos = rep(0.05, nrow(DT)),
  strat = 307L,
  asset = 8001L,
  ctr_step = 0.01,
  lev = 1,
  fee_rt = 0.0005,
  rec = TRUE
)

calc_strategy_performance_summary(
  eq,
  tgt_pos = tgt_pos,
  recorder = attr(eq, "recorder"),
  fee_rt = 0.0005
)

2. Latest-state action plan

state <- list(
  ctr_size = 1.0,
  ctr_step = 1.0,
  lev = 10.0,
  last_px = 100.0,
  ctr_unit = 0.0,
  avg_price = NaN,
  cash = 10000.0,
  pos_dir = 0L
)

plan <- strat_donchian_turtle_action_plan(
  DT,
  state,
  entry_n = 30L,
  exit_n = 10L,
  target_size = 0.95,
  strat_id = 307L
)

3. Warmup-aware strategy mining

walk_res <- mine_strategy_walk_forward(
  DT,
  strategy_fun = strat_ema_triple_trend_tgt_pos,
  param_grid = data.table::CJ(
    fast = c(10L, 20L),
    mid = c(30L, 50L),
    slow = c(80L, 120L),
    target_size = 0.95
  )[fast < mid & mid < slow],
  train_years = 1,
  test_years = 0.5,
  step_years = 0.5,
  n_best = 2L,
  min_train_rows = 100L,
  min_test_rows = 50L,
  warmup_days = 120L,
  strat_id = 106L,
  ctr_step = 0.01,
  lev = 1,
  fee_rt = 0.0005
)

summarize_walk_forward_results(walk_res, group_cols = c("fast", "mid", "slow"))
filter_walk_forward_results(
  walk_res,
  group_cols = c("fast", "mid", "slow"),
  min_windows = 1L,
  min_positive_return_rate = 0.5,
  max_avg_score_decay = 10
)

4. Cross-sectional allocator and portfolio backtest

panel <- CJ(
  date = as.Date("2020-01-01") + 0:9,
  asset = c("AAA", "BBB", "CCC")
)
panel[, score := fifelse(asset == "AAA", 0.8, fifelse(asset == "BBB", 0.4, 0.2)) + as.numeric(date - min(date)) / 100]
panel[, open := 100 + match(asset, c("AAA", "BBB", "CCC")) * 5 + as.numeric(date - min(date))]
panel[, close := open * (1 + score / 100)]
panel[, target_weight := strat_cross_sectional_rank_allocator_tgt_pos(
  panel,
  date_col = "date",
  asset_col = "asset",
  signal_col = "score",
  long_n = 2L,
  short_n = 0L,
  gross_exposure = 1.0
)]

portfolio_bt <- backtest_portfolio_weights(
  panel,
  initial_equity = 100000,
  fee_rt = 0.0005
)

portfolio_bt$equity[, .(date, equity, gross_exposure, turnover, fee_paid)]

5. Fixed-income carry/roll and hedge workflow

bond_dt <- data.table(
  par = rep(100, 5),
  c_rate = rep(0.05, 5),
  maturity = seq(2, 6),
  freq = rep(2, 5),
  ytm = c(0.045, 0.044, 0.043, 0.042, 0.041)
)

carry_tgt <- strat_bond_carry_roll_tgt_pos(
  bond_dt,
  long_threshold = 0,
  short_threshold = -0.02,
  target_size = 0.5
)

bond_state <- calc_bond_risk_state(
  par = 100,
  c_rate = 0.06,
  maturity = 3,
  freq = 2,
  ytm = 0.05,
  accrual_frac = 0.25
)

dv01_plan <- plan_duration_neutral_adjustment(
  current_dv01 = bond_state$dv01 * 1000,
  target_dv01 = 0,
  hedge_dv01 = -125
)

6. Option and volatility workflow

option_chain <- CJ(
  date = as.Date("2020-01-01") + 0:79,
  time_to_expiry = c(30, 60) / 365,
  type = c("put", "call")
)
option_chain[, option_log_forward_moneyness := fifelse(type == "put", -0.1, 0.1)]
option_chain[, close := 100 + as.numeric(date - min(date)) * 0.1]
option_chain[, iv := 0.2 + fifelse(type == "put", 0.03, -0.01) + sin(seq_len(.N) / 20) / 100]

iv_tgt <- strat_iv_directional_overlay_tgt_pos(
  option_chain,
  trend_n = 20L,
  skew_long_threshold = 0.02,
  skew_short_threshold = -0.02,
  overlay_mode = "confirm"
)

option_state <- calc_option_risk_state(
  S = 100,
  K = 100,
  time_to_expiry = 30 / 365,
  r = 0.04,
  sigma = 0.25,
  type = "call"
)

delta_plan <- plan_delta_neutral_adjustment(
  current_delta = option_state$delta * 100,
  target_delta = 0,
  hedge_delta = -50
)

7. Public strategy definitions for Vox

strategy_public_definition("donchian_turtle")
strategy_monitor_definition("donchian_turtle")

strategy_public_definition() is the canonical public-safe source for Vox strategy descriptions and effective default parameters. Parameter values are finite numbers or NULL; NULL explicitly marks an unbounded setting that is disabled by default so definitions remain JSON-safe. strategy_monitor_definition() is the canonical public-safe source for Strategy Monitor family and regime metadata. Current Vox monitor ids are buy_hold, ema_cross_adx, ema_cross_slope_confirm, rsi_revert, vol_target, donchian_turtle, bollinger_revert, and regime_switch.

Docs

Current Scope

feature utilities such as SMA, EMA, WMA, DEMA, ZLEMA, HMA, RSI, SMI, CMO, ROC, MACD, TRIX, KST, ultimate oscillator, ADX, Aroon, ATR, Bollinger bands, Keltner channels, CCI, stochastic oscillator, MFI, OBV, VWAP, VWMA, CMF, Williams %R, EMV, Chaikin AD, Chaikin volatility, Donchian channels, ladder-cycle indexing, rolling statistical features, breadth indicators, relative-value descriptors, execution and microstructure proxies, futures curve features, FX carry and basis features, and option-surface summaries
fixed-income analytics such as bond cash flows, yield, clean and dirty pricing, duration, convexity, DV01/PV01, z-spread, carry and roll-down, curve shocks, key-rate duration, and coupon/convention helpers
option-risk analytics such as Black-Scholes Greeks, implied volatility, position Greek aggregation, option risk-state snapshots, and delta/vega hedge-adjustment helpers
credit and spread features such as credit spread, excess spread, and spread-curve slope and butterfly descriptors
strategy-facing target-position logic
public strategy families covering buy-and-hold, EMA/MACD crossover and MACD contrarian, Bollinger reversion, classic and log-return RSI reversion, Donchian and ATR breakout, volatility targeting, trend pullback, pair-spread and ratio reversion, relative strength, FX carry, bond carry-and-roll, curve steepener, roll yield, IV skew, IV term structure, and options structure-proxy workflows
portfolio-adjustment planning from target weights and current holdings
portfolio target-weight backtesting with open-price rebalancing and close-price mark-to-market, backed by a native accounting core for the standard no-position-path case
fixed-income risk-state snapshots and hedge-adjustment helpers
option risk-state snapshots and hedge-adjustment helpers
action-plan generation from current account state
order-intent tables for rebalancing workflows
path-dependent backtesting with execution and funding assumptions
strategy-mining helpers for Sortino-ranked parameter sweeps, fixed-parameter asset sweeps, asset-year mining with buy-and-hold filtering, walk-forward train/test mining, reusable parameter selection, and walk-forward diagnostics and anti-overfit filtering
native signal kernels for selected high-use path-dependent strategies while preserving R wrappers as the public API

Author

Oliver Zhou oliver.yxzhou@gmail.com

License

MIT License

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