feat: wire 4 institutional flow factors (L2, options, ticks, intraday); stub data committed

.pytest_cache/.gitignore

@@ -0,0 +1,2 @@
+# Created by pytest automatically.
+*

.pytest_cache/CACHEDIR.TAG

@@ -0,0 +1,4 @@
+Signature: 8a477f597d28d172789f06886806bc55
+# This file is a cache directory tag created by pytest.
+# For information about cache directory tags, see:
+#	https://bford.info/cachedir/spec.html

.pytest_cache/README.md

@@ -0,0 +1,8 @@
+# pytest cache directory #
+
+This directory contains data from the pytest's cache plugin,
+which provides the `--lf` and `--ff` options, as well as the `cache` fixture.
+
+**Do not** commit this to version control.
+
+See [the docs](https://docs.pytest.org/en/stable/how-to/cache.html) for more information.

.pytest_cache/v/cache/lastfailed

@@ -0,0 +1,3 @@
+{
+  "tests/test_flow_algo.py::test_uptrend_has_positive_cmf_and_obv": true
+}

.pytest_cache/v/cache/nodeids

@@ -0,0 +1,86 @@
+[
+  "tests/test_flow_algo.py::test_downtrend_has_negative_cmf_and_obv",
+  "tests/test_flow_algo.py::test_empty_frame_returns_none",
+  "tests/test_flow_algo.py::test_factor_dict_round_trip",
+  "tests/test_flow_algo.py::test_factors_finite_and_bounded",
+  "tests/test_flow_algo.py::test_too_short_returns_none",
+  "tests/test_flow_algo.py::test_uptrend_has_positive_cmf_and_obv",
+  "tests/test_flow_algo.py::test_vwap_dev_positive_when_close_above_average",
+  "tests/test_history.py::test_latest_snapshot_returns_newest",
+  "tests/test_history.py::test_save_and_list_snapshots",
+  "tests/test_history.py::test_save_empty_df_returns_none",
+  "tests/test_history.py::test_snapshot_summary_lists_saved_runs",
+  "tests/test_history.py::test_with_delta_computes_difference",
+  "tests/test_history.py::test_with_delta_no_previous",
+  "tests/test_intraday_factor.py::test_aggression_persistence_negative_when_recent_selling",
+  "tests/test_intraday_factor.py::test_aggression_persistence_positive_when_recent_buying",
+  "tests/test_intraday_factor.py::test_aggression_persistence_short_history_uses_all",
+  "tests/test_intraday_factor.py::test_batch",
+  "tests/test_intraday_factor.py::test_clipped_to_range",
+  "tests/test_intraday_factor.py::test_empty_returns_zeros",
+  "tests/test_intraday_factor.py::test_stub_synthesises",
+  "tests/test_intraday_factor.py::test_vwap_dev_positive_when_above_vwap",
+  "tests/test_intraday_factor.py::test_vwap_dev_zero_when_price_equals_vwap",
+  "tests/test_l2_factor.py::test_ask_heavy_book_is_negative",
+  "tests/test_l2_factor.py::test_balanced_book_is_near_zero",
+  "tests/test_l2_factor.py::test_batch_returns_all_tickers",
+  "tests/test_l2_factor.py::test_bid_heavy_book_is_positive",
+  "tests/test_l2_factor.py::test_book_lying_detection",
+  "tests/test_l2_factor.py::test_book_lying_discounts_factor",
+  "tests/test_l2_factor.py::test_clipped_to_range",
+  "tests/test_l2_factor.py::test_empty_book_returns_zero",
+  "tests/test_l2_factor.py::test_spoofed_orders_ignored",
+  "tests/test_l2_factor.py::test_stub_data_source_synthesises",
+  "tests/test_options_factor.py::test_batch",
+  "tests/test_options_factor.py::test_call_otm_spike_is_positive",
+  "tests/test_options_factor.py::test_call_otm_weighted_heaviest",
+  "tests/test_options_factor.py::test_empty_returns_zero",
+  "tests/test_options_factor.py::test_put_otm_spike_is_negative",
+  "tests/test_options_factor.py::test_steady_chain_is_near_zero",
+  "tests/test_options_factor.py::test_stub_data_source_synthesises",
+  "tests/test_options_factor.py::test_zscore_constant_returns_zero",
+  "tests/test_options_factor.py::test_zscore_uses_today_vs_history",
+  "tests/test_performance.py::test_auto_improve_end_to_end_with_signal",
+  "tests/test_performance.py::test_auto_improve_handles_no_data",
+  "tests/test_performance.py::test_collect_eval_tables_with_explicit_cache",
+  "tests/test_performance.py::test_evaluate_returns_metrics_dict",
+  "tests/test_performance.py::test_ic_signal_factor_is_predictive",
+  "tests/test_performance.py::test_load_learned_weights_missing",
+  "tests/test_performance.py::test_load_learned_weights_round_trip",
+  "tests/test_performance.py::test_optimize_finds_signal_factor",
+  "tests/test_performance.py::test_optimize_without_enough_history_returns_baseline",
+  "tests/test_performance.py::test_performance_log_grows",
+  "tests/test_scorer.py::test_empty_factors_returns_empty_df",
+  "tests/test_scorer.py::test_ratings_assignment",
+  "tests/test_scorer.py::test_robust_zscore_degenerate_series",
+  "tests/test_scorer.py::test_robust_zscore_on_outliers",
+  "tests/test_scorer.py::test_score_dataframe_shape",
+  "tests/test_scorer.py::test_score_range_and_ordering",
+  "tests/test_scorer.py::test_top_n_buy_and_sell",
+  "tests/test_scorer.py::test_uptrend_scores_higher_than_downtrend",
+  "tests/test_scorer.py::test_weights_change_ordering",
+  "tests/test_tick_factor.py::test_batch",
+  "tests/test_tick_factor.py::test_block_aggression_negative_when_block_sells",
+  "tests/test_tick_factor.py::test_block_aggression_positive_when_block_buys",
+  "tests/test_tick_factor.py::test_block_share_calculation",
+  "tests/test_tick_factor.py::test_bucket_boundaries",
+  "tests/test_tick_factor.py::test_buy_ratio_in_unit_interval",
+  "tests/test_tick_factor.py::test_empty_ticks",
+  "tests/test_tick_factor.py::test_no_bid_ask_falls_back_to_rolling_mid",
+  "tests/test_tick_factor.py::test_sign_at_ask_is_buy",
+  "tests/test_tick_factor.py::test_sign_at_bid_is_sell",
+  "tests/test_tick_factor.py::test_sign_at_mid_carries_forward",
+  "tests/test_tick_factor.py::test_stub_synthesises",
+  "tests/test_universe.py::test_cached_sectors_for_returns_only_known",
+  "tests/test_universe.py::test_fresh_filter_excludes_stale",
+  "tests/test_universe.py::test_save_and_load_cache",
+  "tests/test_watchlist.py::test_load_malformed_returns_empty",
+  "tests/test_watchlist.py::test_load_missing_returns_empty",
+  "tests/test_watchlist.py::test_parse_caps_at_max",
+  "tests/test_watchlist.py::test_parse_empty_or_none",
+  "tests/test_watchlist.py::test_parse_lowercase_and_dedup",
+  "tests/test_watchlist.py::test_parse_mixed_delimiters",
+  "tests/test_watchlist.py::test_parse_rejects_invalid_symbols",
+  "tests/test_watchlist.py::test_parse_simple",
+  "tests/test_watchlist.py::test_save_and_load_round_trip"
+]

README.md

@@ -21,20 +21,30 @@ institutional-style buying or selling pressure.  Designed to run on
 
 ## What it does
 
-For each of ~5000+ US-listed common stocks, the app computes 5 factors and
-combines them into a single **-100 to +100 institutional-flow score**:
-
-| Factor          | What it measures                                              |
-|-----------------|---------------------------------------------------------------|
-| **CMF(20)**     | Chaikin Money Flow over 20 days                               |
-| **OBV slope**   | 20-day linear regression of On-Balance Volume, normalized     |
-| **Big-bar ratio** | Unusual-volume + wide-range bars today vs 20-day baseline   |
-| **VWAP dev**    | Close vs 20-day volume-weighted average price                 |
-| **RVOL signed** | Relative volume, sign-flipped by CMF direction                |
+For each of ~5000+ US-listed common stocks, the app computes **9 factors**
+(5 daily flow proxies + 4 "real" institutional-flow signals) and combines
+them into a single **-100 to +100 institutional-flow score**:
+
+| Factor               | What it measures                                              |
+|----------------------|---------------------------------------------------------------|
+| **CMF(20)**          | Chaikin Money Flow over 20 days                               |
+| **OBV slope**        | 20-day linear regression of On-Balance Volume, normalized     |
+| **Big-bar ratio**    | Unusual-volume + wide-range bars today vs 20-day baseline     |
+| **VWAP dev**         | Close vs 20-day volume-weighted average price                 |
+| **RVOL signed**      | Relative volume, sign-flipped by CMF direction                |
+| **L2 imbalance**     | Top-of-book depth + large resting orders (with spoofing discount) |
+| **Unusual options**  | Vol/OI z-score across 20 days, weighted by moneyness          |
+| **Block aggression** | Net buy/sell bias in ≥10k-share trades (Lee-Ready tick rule)  |
+| **Buy persistence**  | Buy-ratio average over last 1 hour of 5-min bars              |
 
 Factors are z-scored cross-sectionally (median + MAD robust normalization)
 so the most-extreme name in the universe gets ±100.
 
+The last 4 factors come from a pluggable `FactorDataSource` (see
+[Data sources](#data-sources) below).  By default they are computed from
+synthetic stub data committed to the repo, so the app works on
+Hugging Face Spaces with **no live market feed**.
+
 **Rating thresholds** (tweakable in the UI):
 - `≥ +50` → **Strong Buy**
 - `+20 … +49` → **Buy**
@@ -42,9 +52,11 @@ so the most-extreme name in the universe gets ±100.
 - `-49 … -20` → **Sell**
 - `≤ -50` → **Strong Sell**
 
-The default weights are `0.30 / 0.25 / 0.20 / 0.15 / 0.10` for
-`CMF / OBV / big-bar / VWAP / RVOL`.  All sliders are exposed in the
-sidebar accordion.
+The default weights are
+`0.20 / 0.15 / 0.10 / 0.10 / 0.05` for the daily flow factors and
+`0.15 / 0.10 / 0.10 / 0.05` for the institutional factors.
+The 5 daily sliders are exposed in the sidebar accordion; the 4
+institutional factors always run at their default weights.
 
 ## Project layout
 
@@ -55,7 +67,12 @@ us-flow-scanner/
 │   ├── __init__.py
 │   ├── universe.py              # Load & filter ~5000 US tickers
 │   ├── data_fetcher.py          # yfinance + Polygon fallback + cache
-│   ├── flow_algo.py             # 5-factor computation
+│   ├── flow_algo.py             # 5 daily-flow factors
+│   ├── l2_factor.py             # Level-2 large-resting-order factor
+│   ├── options_factor.py        # Unusual options activity
+│   ├── tick_factor.py           # Lee-Ready tick + size buckets
+│   ├── intraday_factor.py       # Intraday VWAP + buy persistence
+│   ├── factor_sources.py        # StubDataSource (default) + FutuDataSource (live)
 │   ├── scorer.py                # Cross-sectional z-score + composite
 │   ├── persistence.py           # Optional HF Dataset round-trip
 │   ├── history.py               # Per-scan snapshots + delta vs prior
@@ -63,7 +80,8 @@ us-flow-scanner/
 │   ├── performance.py           # IC evaluation + self-tuning weights
 │   └── paths.py                 # Centralised, env-var-overridable paths
 ├── data/
-│   └── us_tickers.csv           # ~5260 US common stocks (committed)
+│   ├── us_tickers.csv           # ~5260 US common stocks (committed)
+│   └── stubs/                   # 30-ticker synthetic L2/options/ticks/intraday
 ├── tests/                       # pytest suite (isolated disk paths)
 ├── requirements.txt
 ├── requirements-dev.txt
@@ -80,6 +98,24 @@ python app.py
 
 Then open http://localhost:7860.
 
+## Data sources
+
+The four institutional-flow factors (`l2_imbalance`, `unusual_options`,
+`block_aggression`, `buy_persistence`) read from a pluggable
+`FactorDataSource` (`scanner/factor_sources.py`).
+
+| Source              | When to use                                              | Config env var             |
+|---------------------|----------------------------------------------------------|----------------------------|
+| `StubDataSource`    | **Default.** Reads `data/stubs/` synthetic data          | `FSCANNER_DATA_SOURCE` unset or `=stub` |
+| `FutuDataSource`    | Live Level-2 / options / tick data via local Futu OpenD  | `FSCANNER_DATA_SOURCE=futu` + `FUTU_OPEND_HOST` + `FUTU_OPEND_PORT` |
+
+The Space uses `StubDataSource` so it never depends on a live feed.
+To regenerate the stub data:
+
+```bash
+python data/stubs/_build_stubs.py
+```
+
 ## Tests
 
 ```bash

app.py

@@ -49,10 +49,15 @@ except Exception as _patch_err:  # never let this crash startup
     print(f"[gradio-client patch skipped: {_patch_err}]", file=sys.stderr)
 
 from scanner.data_fetcher import fetch_ohlcv
+from scanner.factor_sources import get_data_source
 from scanner.flow_algo import compute_factors
 from scanner.history import (
     latest_snapshot, save_snapshot, snapshot_summary, with_delta,
 )
+from scanner.intraday_factor import compute_intraday_factors_batch
+from scanner.l2_factor import compute_l2_factors
+from scanner.options_factor import compute_options_factors
+from scanner.tick_factor import compute_tick_factors_batch
 from scanner import paths
 from scanner.performance import (
     HORIZON_DAYS, auto_improve, load_learned_meta,
@@ -110,7 +115,8 @@ def _format_status() -> str:
 def _result_columns() -> list[str]:
     return ["ticker", "name", "rating", "score", "score_delta", "last_close",
             "adv_dollar", "cmf", "obv_slope", "big_bar_ratio", "vwap_dev",
-            "rvol_signed"]
+            "rvol_signed", "l2_imbalance", "unusual_options",
+            "block_aggression", "buy_persistence"]
 
 
 def _df_for_display(df: Optional[pd.DataFrame]) -> Optional[pd.DataFrame]:
@@ -468,8 +474,41 @@ def _do_scan(
     if not factors:
         return None, valid_frames, scanned_universe, "No tickers had enough data."
 
+    # ----------------------------------------------------------------
+    # Institutional-flow factors (L2, options, ticks, intraday).
+    # Computed from the configured FactorDataSource (StubDataSource by
+    # default; set FSCANNER_DATA_SOURCE=futu for live Futu OpenD).  Any
+    # data-source exception is swallowed - a missing real feed just
+    # leaves the new factors at neutral (0.0) and the scan still
+    # produces a valid ranking from the original 5 flow factors.
+    # ----------------------------------------------------------------
+    progress(0.92, desc="Computing institutional factors (L2/options/ticks/intraday)...")
+    extra_factors = {"l2_imbalance": {}, "unusual_options": {},
+                     "block_aggression": {}, "buy_persistence": {}}
+    inst_tickers = [fs.ticker for fs in factors]
+    try:
+        source = get_data_source()
+        l2 = compute_l2_factors(inst_tickers, source=source)
+        extra_factors["l2_imbalance"] = l2
+        opt = compute_options_factors(inst_tickers, source=source)
+        extra_factors["unusual_options"] = opt
+        ticks_df = compute_tick_factors_batch(inst_tickers, source=source)
+        for t in inst_tickers:
+            if t in ticks_df.index:
+                extra_factors["block_aggression"][t] = float(
+                    ticks_df.loc[t, "block_aggression"]
+                )
+        intraday_df = compute_intraday_factors_batch(inst_tickers, source=source)
+        for t in inst_tickers:
+            if t in intraday_df.index:
+                extra_factors["buy_persistence"][t] = float(
+                    intraday_df.loc[t, "aggression_persistence"]
+                )
+    except Exception as e:
+        print(f"[institutional factors skipped: {e}]", file=sys.stderr)
+
     progress(0.94, desc="Scoring..." )
-    df = score_factors(factors, weights=weights)
+    df = score_factors(factors, weights=weights, extra_factors=extra_factors)
 
     # Attach names
     name_map = dict(zip(universe["ticker"], universe["name"]))

data/stubs/_build_stubs.py

@@ -0,0 +1,175 @@
+"""Build the bundled synthetic stub data for the four institutional factors.
+
+Run locally to regenerate::
+
+    python data/stubs/_build_stubs.py
+
+This produces realistic-but-synthetic Level-2, options, tick, and
+intraday data for ~30 representative tickers.  The Space uses this
+data so the math can be demonstrated without any live market feed.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import os
+import random
+import sys
+from datetime import datetime, timedelta
+
+import numpy as np
+import pandas as pd
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+PROJ = os.path.dirname(os.path.dirname(HERE))
+sys.path.insert(0, PROJ)
+
+
+SAMPLE_TICKERS = [
+    # Mega caps (high L2 liquidity, deep options)
+    "AAPL", "MSFT", "NVDA", "AMZN", "GOOGL", "META", "TSLA", "AVGO",
+    # Mid caps (still liquid, smaller options chains)
+    "AMD", "NFLX", "CRM", "ORCL", "ADBE", "INTC", "QCOM",
+    # Sector leaders
+    "JPM", "BAC", "GS", "MS", "V", "MA", "JNJ", "PFE", "UNH", "XOM", "CVX",
+    # Recent IPOs / volatile names (unusual options activity test)
+    "PLTR", "GME", "AMC", "RIOT",
+]
+
+
+def _rng(ticker: str, salt: str = "") -> random.Random:
+    return random.Random(f"{ticker}-{salt}")
+
+
+def build_l2(out_dir: str) -> None:
+    os.makedirs(out_dir, exist_ok=True)
+    for t in SAMPLE_TICKERS:
+        rng = _rng(t, "l2")
+        # Each ticker gets a "lean" direction: positive = bullish, negative = bearish
+        # Distribute so ~40% bullish, ~40% bearish, ~20% neutral.
+        lean = rng.choices([-1, -0.5, 0, 0.5, 1], weights=[0.2, 0.2, 0.2, 0.2, 0.2])[0]
+        mid = rng.uniform(20, 500)
+        spread = mid * 0.0005
+        bids, asks = [], []
+        for i in range(10):
+            bp = mid - spread / 2 - i * spread * 0.5
+            ap = mid + spread / 2 + i * spread * 0.5
+            # Lean shifts size distribution: bullish = bigger bids; bearish = bigger asks
+            bs = int(rng.lognormvariate(6 + lean * 0.5, 1.0))
+            as_ = int(rng.lognormvariate(6 - lean * 0.5, 1.0))
+            age = round(rng.uniform(1.5, 30.0), 1)  # all stable
+            bids.append([round(bp, 2), bs, "NSDQ", age])
+            asks.append([round(ap, 2), as_, "NSDQ", round(rng.uniform(1.5, 30.0), 1)])
+        out = {
+            "ticker": t,
+            "ts": datetime.utcnow().isoformat() + "Z",
+            "bids": bids,
+            "asks": asks,
+        }
+        with open(os.path.join(out_dir, f"{t}.json"), "w", encoding="utf-8") as fh:
+            json.dump(out, fh, indent=2)
+
+
+def build_options(out_dir: str) -> None:
+    os.makedirs(out_dir, exist_ok=True)
+    today = pd.Timestamp.utcnow().tz_localize(None).normalize()
+    for t in SAMPLE_TICKERS:
+        rng = _rng(t, "opt")
+        rows = []
+        # Some tickers have unusual activity on a "spike day" in the last week
+        spike_day_offset = rng.randint(1, 5)
+        spike_side = rng.choice(["call", "put"])
+        for d in range(20):
+            date = today - pd.Timedelta(days=d)
+            for kind in ("call", "put"):
+                for bucket in ("itm", "atm", "otm"):
+                    base = rng.lognormvariate(7, 0.6)
+                    # Spike day: 3-5x normal volume for the chosen side
+                    spike = 1.0
+                    if d == spike_day_offset and kind == spike_side:
+                        spike = rng.uniform(3.0, 6.0)
+                    rows.append({
+                        "date": date,
+                        "kind": kind,
+                        "moneyness": bucket,
+                        "volume": int(base * spike * rng.uniform(0.7, 1.3)),
+                        "oi": int(base * rng.uniform(3, 10)),
+                        "avg_iv": rng.uniform(0.18, 0.65),
+                    })
+        df = pd.DataFrame(rows)
+        df.to_parquet(os.path.join(out_dir, f"{t}.parquet"), index=False)
+
+
+def build_ticks(out_dir: str) -> None:
+    os.makedirs(out_dir, exist_ok=True)
+    for t in SAMPLE_TICKERS:
+        rng = _rng(t, "ticks")
+        n = rng.randint(800, 1500)
+        base = rng.uniform(20, 500)
+        ts0 = pd.Timestamp("2026-06-02") + pd.Timedelta(hours=9, minutes=30)
+        rows = []
+        price = base
+        for i in range(n):
+            dt = pd.Timedelta(seconds=i * 1.5 + rng.uniform(0, 1.5))
+            price *= 1 + rng.gauss(0, 0.0005)
+            spread = price * 0.0003
+            sz = int(rng.choices(
+                [50, 100, 200, 500, 1000, 5000, 10000, 20000],
+                weights=[0.25, 0.25, 0.15, 0.15, 0.10, 0.05, 0.03, 0.02],
+            )[0])
+            rows.append({
+                "ts": ts0 + dt,
+                "price": round(price, 4),
+                "size": sz,
+                "bid": round(price - spread / 2, 4),
+                "ask": round(price + spread / 2, 4),
+            })
+        pd.DataFrame(rows).to_parquet(os.path.join(out_dir, f"{t}.parquet"), index=False)
+
+
+def build_intraday(out_dir: str) -> None:
+    os.makedirs(out_dir, exist_ok=True)
+    for t in SAMPLE_TICKERS:
+        rng = _rng(t, "intra")
+        bars = []
+        day = pd.Timestamp("2026-06-02") + pd.Timedelta(hours=9, minutes=30)
+        price = rng.uniform(20, 500)
+        # Each ticker has an aggression lean
+        buy_bias = rng.gauss(0, 0.08)
+        for i in range(78):
+            ts = day + pd.Timedelta(minutes=i * 5)
+            o = price
+            ret = rng.gauss(0, 0.003)
+            c = o * (1 + ret)
+            h = max(o, c) * (1 + abs(rng.gauss(0, 0.0015)))
+            l = min(o, c) * (1 - abs(rng.gauss(0, 0.0015)))
+            v = int(rng.lognormvariate(13, 0.6))
+            ratio = 0.5 + buy_bias + rng.gauss(0, 0.06)
+            ratio = max(0.30, min(0.70, ratio))
+            bv = int(v * ratio)
+            bars.append({
+                "bar_start": ts, "open": o, "high": h, "low": l, "close": c,
+                "volume": v, "buy_vol": bv, "sell_vol": v - bv,
+            })
+            price = c
+        pd.DataFrame(bars).to_parquet(os.path.join(out_dir, f"{t}.parquet"), index=False)
+
+
+def main() -> None:
+    print(f"Building stub data in {HERE}/")
+    for fn, name in [
+        (build_l2, "l2"),
+        (build_options, "options"),
+        (build_ticks, "ticks"),
+        (build_intraday, "intraday"),
+    ]:
+        path = os.path.join(HERE, name)
+        fn(path)
+        files = len(os.listdir(path))
+        print(f"  {name}: {files} files")
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()

data/stubs/intraday/AAPL.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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data/stubs/l2/ADBE.json

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data/stubs/l2/AMD.json

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data/stubs/l2/AMZN.json

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+}

data/stubs/l2/AVGO.json

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+    ],
+    [
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+      "NSDQ",
+      22.8
+    ],
+    [
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+      173,
+      "NSDQ",
+      5.4
+    ]
+  ]
+}

data/stubs/l2/BAC.json

@@ -0,0 +1,128 @@
+{
+  "ticker": "BAC",
+  "ts": "2026-06-03T02:13:03.098593Z",
+  "bids": [
+    [
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+      344,
+      "NSDQ",
+      16.9
+    ],
+    [
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+    ],
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+    ],
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+    ],
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+    ],
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+    ],
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+    ],
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+    ],
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+    ],
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+    ]
+  ],
+  "asks": [
+    [
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+    ],
+    [
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+    ],
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+    ],
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+    ],
+    [
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+    ],
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+    ],
+    [
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+    ],
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+    ],
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+    ],
+    [
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+      "NSDQ",
+      18.6
+    ]
+  ]
+}

data/stubs/l2/CRM.json

@@ -0,0 +1,128 @@
+{
+  "ticker": "CRM",
+  "ts": "2026-06-03T02:13:03.098593Z",
+  "bids": [
+    [
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+      314,
+      "NSDQ",
+      15.7
+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+    ],
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+    ],
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+    ],
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
+      12.7
+    ],
+    [
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+    ],
+    [
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+    ],
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+      "NSDQ",
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+    ]
+  ],
+  "asks": [
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+      "NSDQ",
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+    ],
+    [
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+    ],
+    [
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+    ],
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      1060,
+      "NSDQ",
+      11.3
+    ]
+  ]
+}

data/stubs/l2/CVX.json

@@ -0,0 +1,128 @@
+{
+  "ticker": "CVX",
+  "ts": "2026-06-03T02:13:03.098593Z",
+  "bids": [
+    [
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+      224,
+      "NSDQ",
+      20.5
+    ],
+    [
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+      "NSDQ",
+      21.1
+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
+      25.6
+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
+      13.8
+    ]
+  ],
+  "asks": [
+    [
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+      "NSDQ",
+      11.4
+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
+      7.7
+    ],
+    [
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+      "NSDQ",
+      8.6
+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
+      10.2
+    ],
+    [
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+      "NSDQ",
+      23.4
+    ],
+    [
+      347.93,
+      573,
+      "NSDQ",
+      17.5
+    ]
+  ]
+}

data/stubs/l2/GME.json

@@ -0,0 +1,128 @@
+{
+  "ticker": "GME",
+  "ts": "2026-06-03T02:13:03.098593Z",
+  "bids": [
+    [
+      146.47,
+      288,
+      "NSDQ",
+      9.6
+    ],
+    [
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+      298,
+      "NSDQ",
+      21.3
+    ],
+    [
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+      140,
+      "NSDQ",
+      10.5
+    ],
+    [
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+      374,
+      "NSDQ",
+      18.6
+    ],
+    [
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+      "NSDQ",
+      4.4
+    ],
+    [
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+      1620,
+      "NSDQ",
+      10.3
+    ],
+    [
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+      "NSDQ",
+      12.0
+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
+      19.5
+    ]
+  ],
+  "asks": [
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
+      6.2
+    ],
+    [
+      146.87,
+      177,
+      "NSDQ",
+      29.5
+    ]
+  ]
+}

data/stubs/l2/GOOGL.json

@@ -0,0 +1,128 @@
+{
+  "ticker": "GOOGL",
+  "ts": "2026-06-03T02:13:03.094736Z",
+  "bids": [
+    [
+      481.63,
+      238,
+      "NSDQ",
+      16.6
+    ],
+    [
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+      269,
+      "NSDQ",
+      21.0
+    ],
+    [
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+      1355,
+      "NSDQ",
+      10.5
+    ],
+    [
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+      "NSDQ",
+      14.5
+    ],
+    [
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+      "NSDQ",
+      12.3
+    ],
+    [
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+      115,
+      "NSDQ",
+      17.2
+    ],
+    [
+      480.91,
+      131,
+      "NSDQ",
+      17.7
+    ],
+    [
+      480.78,
+      679,
+      "NSDQ",
+      6.3
+    ],
+    [
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+      "NSDQ",
+      29.2
+    ],
+    [
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+      "NSDQ",
+      20.9
+    ]
+  ],
+  "asks": [
+    [
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+      1097,
+      "NSDQ",
+      26.7
+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
+      10.3
+    ],
+    [
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+      "NSDQ",
+      5.6
+    ],
+    [
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+      "NSDQ",
+      12.1
+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
+      2.5
+    ],
+    [
+      482.95,
+      4857,
+      "NSDQ",
+      20.9
+    ]
+  ]
+}

data/stubs/l2/GS.json

@@ -0,0 +1,128 @@
+{
+  "ticker": "GS",
+  "ts": "2026-06-03T02:13:03.098593Z",
+  "bids": [
+    [
+      203.18,
+      123,
+      "NSDQ",
+      4.4
+    ],
+    [
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+      163,
+      "NSDQ",
+      18.2
+    ],
+    [
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+      1150,
+      "NSDQ",
+      2.9
+    ],
+    [
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+      23,
+      "NSDQ",
+      2.9
+    ],
+    [
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+      "NSDQ",
+      5.6
+    ],
+    [
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+      2206,
+      "NSDQ",
+      8.2
+    ],
+    [
+      202.88,
+      784,
+      "NSDQ",
+      15.6
+    ],
+    [
+      202.83,
+      515,
+      "NSDQ",
+      9.6
+    ],
+    [
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+      "NSDQ",
+      24.2
+    ],
+    [
+      202.73,
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+      "NSDQ",
+      6.2
+    ]
+  ],
+  "asks": [
+    [
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+      "NSDQ",
+      28.9
+    ],
+    [
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+      "NSDQ",
+      27.3
+    ],
+    [
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+      "NSDQ",
+      5.8
+    ],
+    [
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+      "NSDQ",
+      16.3
+    ],
+    [
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+      "NSDQ",
+      16.1
+    ],
+    [
+      203.54,
+      217,
+      "NSDQ",
+      7.9
+    ],
+    [
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+      128,
+      "NSDQ",
+      28.6
+    ],
+    [
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+      "NSDQ",
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+    ],
+    [
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+      "NSDQ",
+      11.0
+    ],
+    [
+      203.74,
+      94,
+      "NSDQ",
+      20.8
+    ]
+  ]
+}