A Veridex deploy is a pinned strategy instance built from a template and a config. The AgentTemplate is the strategy family — the signal logic, decision algorithm, and market regime it targets. The AgentConfig is the concrete deployed instance: the specific thresholds, lookback windows, market universe, sizing rules, and execution mode you set. Two users can deploy the same template with different configs and get different CLV, PnL, hit rate, and drawdown — that is the point of Agent Studio.
Available templates
The following strategy templates are available in Veridex. Each is a separate, deterministic, proposer-only agent: it proposes an AgentAction; the deterministic law recomputes edge and CLV from sealed evidence. No template self-certifies its own score.
Template key: momentum
Class: SharpMomentumStrategy / SharpMomentumAgent (v2)
File: veridex/strategies/momentum.py
Proof mode: reproducibleA false-positive-controlled line-movement detector grounded in the line-movement literature (Simon 2024, Management Science). It does not predict outcomes — it detects sustained, statistically significant TxLINE repricing and proposes the side the market is confirming.The detection pipeline, in order:
- Logit-space movements — probabilities are converted to log-odds before measurement, so a 2%→3% and a 50%→51% move are correctly treated as different-sized events.
- EWMA smoothing (
alpha) — denoises single-tick spikes before the robust-z gate.
- Robust z-score (median/MAD,
scale_floor-floored) of the latest smoothed movement vs its recent window — immune to a lone outlier. The scale_floor prevents a flat market from dividing by a near-zero MAD when it suddenly reprices.
- Directional Page-Hinkley change-point (
ph_delta, ph_lambda) — confirms the move is sustained and in the same direction as the shock. A downward change-point cannot confirm an upward move.
- Persistence confirmation — at least 2 of the last 3 smoothed movements must share the shock’s direction AND their cumulative logit move must clear
persistence_logit.
- Per-market cooldown (
cooldown_ticks) — suppresses a refire for cooldown_ticks ticks after the market fires.
A side is flagged only when all gates pass (AND, not OR). A lone Page-Hinkley trip or lone persistence run does not fire — the false-positive reduction is the point. Operates on 1X2 and totals market families only (props are deferred).Configurable parameters: alpha, z_threshold, ph_delta, ph_lambda, cooldown_ticks, warmup_ticks, min_movements, lookback, scale_floor, persistence_logit — 10 parameters total, all folded into config_hash. Template key: cumulative-drift
Class: CumulativeDriftAgent
File: veridex/strategies/drift.py
Proof mode: reproducibleA smooth, multi-day repricing detector — the complementary regime to Sharp Momentum v2. Where momentum catches abrupt sharp-money moves, cumulative-drift catches smooth, sustained repricing where the de-vigged probability grinds monotonically in one direction over many ticks or days.The detection pipeline:
- Cumulative logit drift —
logit(latest) − logit(first) over the side’s observed window. A genuine sustained repricing accrues a large same-signed cumulative logit move.
- EWMA-slope trend strength (
ewma_slope_alpha) — an EWMA of per-tick drift direction (+1 up / −1 down / 0 flat). A smooth monotone rise smooths toward +1; choppy noise smooths toward 0. This is the “is it actually trending?” gate that separates a real drift from a random walk that merely ends higher.
A side fires only when: it has been observed at least min_tick_count times, its observation horizon spans at least min_horizon_s seconds, its cumulative logit drift clears cum_drift_logit_min, and its EWMA-slope trend strength clears trend_strength_min. This is the thin-data guard — on a short or fast-but-brief tape, the agent stays quiet.Configurable parameters: cum_drift_logit_min, ewma_slope_alpha, trend_strength_min, min_tick_count, min_horizon_s, close_quality_required, cooldown_ticks.The CumulativeDriftAgent produced the strongest result in the Veridex World Cup backtest (Run-001: +61.19 bps avg CLV across 18 fixtures, beating all three deterministic baselines on that sealed sample). This result did not survive out-of-sample testing. The surviving sub-signal falsified on its own metric and came out NULL on independent settled outcomes at only N=2 effective fixtures. The Run-001 figure is directional evidence that the agent beat its baselines on identical sealed inputs; it is not a statistical proof of edge and should not be read as proven profitability or validated alpha.
Template key: value
Class: ValueVsVenue (vvv_signal)
File: veridex/strategies/value_vs_venue.py
Proof mode: reproducibleA fair-value-vs-venue agent that compares TxLINE’s de-margined consensus fair probability against an injected venue decimal price to surface an estimated executable edge:The venue price enters only through an injected VenuePriceSource, keyed by (fixture_id, market_key, side, ts) — time-aligned to the tick’s decision coordinate. When no quote is available within the freshness bound, the agent returns WAIT rather than act on a stale price.Important trust boundary: The estimated executable edge and venue price are not sealed into the evidence hash and are never scored. The action carries only TxLINE-derived fields (market_key, side, and a static number-free reason string). The edge number is explanatory context, not a ranked axis. Template key: llm
Class: LLMDriftCheckpointRunner (LLM-Drift)
File: veridex/strategies/llm_drift.py
Proof mode: LLM/evidence-verifiedAn LLM-guided directional agent that lets a model interpret the same DriftFeatureSnapshot that the deterministic CumulativeDriftAgent uses — a rules-vs-reasoning comparison on identical evidence and identical decision opportunities.The model receives only the deterministic, trusted-code-computed feature snapshot; it has no history, no memory, and no tools (tools=[] is a hard invariant). The snapshot’s evidence_hash is computed before the call and embedded as the call’s evidence coordinate. Any rationale, confidence, or claimed edge the model places in params is untrusted display metadata — the deterministic law recomputes edge and CLV from sealed evidence and does not trust the model’s numbers.The LLM is outside the audited trust path. The trust path is import-audited to contain zero LLM SDK code. The model proposes; the law scores. The proof_mode for LLM-Drift runs is LLM/evidence-verified rather than reproducible.The checkpoint state machine allows one in-flight LLM call at a time. A completed-fresh call produces at most one scored action; a completed-stale call is dropped. Every model response is revalidated into a typed AgentAction before it can affect anything.
Template key: baseline
File: veridex/backtest/baseline_agents.py
Proof mode: reproducibleDeterministic baseline agents used for benchmarking. The three baselines included in the World Cup backtest (Run-001) were: favorite-follower (+4.6 bps avg CLV), threshold-move (−126.5 bps), and seeded-random (−341.7 bps). Baselines run through the same law, policy, and proof pipeline as any other template — their results are just as verifiable and just as falsifiable.
Key configurable parameters
The following parameters appear across templates and in sample_agent.toml. All strategy-affecting parameters are folded into the config_hash.
The config_hash
Every strategy parameter you set is folded into a deterministic config_hash at deploy time. Changing any strategy parameter creates a new pinned config hash and therefore a new AgentInstance.
This is intentional. Because each config produces a unique sealed identity, Veridex can replay exactly which config caused which actions. Two agents on the same template with different configs are different instances with different hashes — and the leaderboard reflects that.
Extend with your own strategy
You can add a custom strategy through three documented extension seams:
Test new configs through replay or backtest mode before promoting to paper or live runs. This gives you a visible performance and policy record before your config reaches real venue submission.
