# /ES Multi-Timeframe Confluence Chart — Implementation Plan **Audience:** the implementing agent. This document is the spec; it is written to be executed top-to-bottom without re-deriving decisions. **One-line goal:** stream `/ES` 1-minute bars from Schwab, aggregate them into every larger timeframe locally, derive trendlines and moving averages on each timeframe, project them all onto one chart in a shared `(time, price)` coordinate system, and alert when independently-derived levels from different timeframes converge. **Explicitly out of scope:** order execution. Nothing in this codebase places a trade. See [§14](#14-why-execution-is-out-of-scope) for why, and for the seam left behind. --- ## 0. Start here **Read §1, §2.1, §6, and §13 before writing anything.** The rest can be read as you reach each milestone. **Work on a branch — do not push to `main`.** `main` is wired to a Forgejo webhook that triggers a Coolify production deploy at . Pushing to main ships whatever you wrote. Branch: `feat/chart-engine`. **Build order is M0 → M1 → M2 → M3 → M3.5 → M4 → M5.** Stop after M5 and get feedback; M6+ are separately scoped. No API keys are required for any of M0–M5. **Existing repo state:** a placeholder FastAPI + Vue 3 (CDN, no build step) app. `main.py` serves `static/index.html` and two toy `/api` endpoints. The serving and deploy wiring is correct and should not be redesigned — extend it. The toy `/api/hello` endpoint and its frontend button can be deleted. ### Dependencies to add `requirements.txt` currently contains only `fastapi` and `uvicorn[standard]`. Add: ``` httpx # Yahoo fetches; async, already a FastAPI-adjacent standard pydantic-settings # config.py ``` `requirements-dev.txt`: ``` pytest pytest-asyncio ``` Do **not** add `schwab-py` until M6 — it is unused before then. Do **not** add `yfinance`; the Yahoo chart endpoint is a plain HTTP GET and the extra dependency buys nothing (verified — see §2.1). ### Test fixture already provided `tests/fixtures/yahoo_es_1h.json` is a **real, trimmed** Yahoo response for `ES=F&interval=1h` (40 bars). Use it to unit-test the parser offline. It deliberately **contains a `null` in the OHLC arrays**, which is exactly the case §2.1 warns about — if your parser doesn't filter those, that fixture will catch it. ### Conventions - All times are **epoch seconds, UTC**, everywhere. No naive datetimes. - Nothing outside `market/` may know which data source is in use. - Nothing outside `market/schwab.py` may import broker-specific code. - Analysis code takes lists of bars and returns values — no I/O, no clocks. This is what makes the replay harness work. --- ## 1. Decisions already made — do not relitigate | Decision | Choice | Why | |---|---|---| | Backend | FastAPI (already scaffolded) | Repo already runs it; native WebSocket support | | Frontend | Vue 3 from CDN, **no build step** | Matches existing `static/` setup; keeps deploy trivial | | Charting | TradingView Lightweight Charts **v5.2.0**, standalone build | Apache-2.0, canvas, built for incremental realtime updates | | Data source | **Pluggable `MarketDataSource`.** Yahoo first, Schwab later | Yahoo needs no API key *and* has the history Schwab lacks — see §2.1 | | Persistence | **In-memory first**, behind a `BarStore` interface | User confirmed deferring persistence is fine for v1 | | Eventual persistence | **SQLite**, not Postgres | Single file, zero Coolify stack expansion. Revisit only if multi-process | | Deployment | **Local first**, VPS later | Keep all config in env vars so the VPS move is config, not rewrite | | Alerts | ntfy/Pushover phone push (+ free in-browser sound) | User selected phone push | | Auth | Hardcoded shared secret from env | User confirmed; only matters once VPS-exposed | | Trendlines | **Manual (hand-drawn) first.** Auto-detection deferred to M8 | Hand-drawn lines are correct by definition, so they validate the confluence engine with zero tuning risk — and later become the ground truth the auto-detector is tuned against | | Moving averages | **Daily set (10/20/50/100/200 SMA) is the priority**, shown on 1m / 30m / 1d | The 200DMA is a level people actually trade against | ### Timeframe roles > **4h was removed from the product on 2026-08-10.** It was never enabled, and > dropping it deleted the fiddliest logic in `session.py` — the wall-clock ET 4h > anchor and its DST edge cases — for a timeframe nobody was using. Later > sections of this document still use 4h in examples; read those as > illustrative, not as a spec to build. Nothing below a day is session-anchored > any more, so `bucket_start` now special-cases only `1d`. ``` 1m base chart + alert evaluation weight 1 ← a switchable base timeframe 2m display only 5m manual lines weight 1 15m manual lines weight 2 30m base chart + manual lines weight 3 ← a switchable base timeframe 1h manual lines (+ optional MAs) weight 4 1d base chart + THE DAILY MAs weight 16 ← a switchable base timeframe ``` Base timeframe controls the candles only. **Every level stays visible on every base timeframe** — the 200DMA on a 1-minute chart is the point, not a side effect. --- ## 2.1 Data sources — build against Yahoo, swap in Schwab later **Do not block on Schwab API keys.** The two sources are complementary, and the Yahoo one is strictly easier to develop against: | | Yahoo `ES=F` | Schwab `/ES` | |---|---|---| | Auth | none — plain HTTP GET | OAuth, keys, 7-day token refresh | | Realtime | polled, possibly ~10 min delayed | true push websocket, realtime | | 1m history | **8 days** (per-request cap) | ❌ none | | 1h history | **~730 days** (17,387 bars, verified back to 2024-03) | ❌ none | | 1d history | **~10 years** (2,517 bars, verified back to 2016) | ❌ none | | Contract | continuous front-month, roll gaps | true contract | Endpoint, verified working with no key and no `yfinance` dependency: ``` https://query1.finance.yahoo.com/v8/finance/chart/ES=F?interval=1h&range=730d ``` Requires a browser `User-Agent` header. Returns `chart.result[0]` with `timestamp[]` and `indicators.quote[0].{open,high,low,close,volume}` as parallel arrays. **Those arrays contain `null` holes — filter them before constructing `Bar`s.** Therefore define one protocol and two implementations: ```python class MarketDataSource(Protocol): name: str def supports_history(self) -> bool: ... async def history(self, symbol, tf, start, end) -> list[Bar]: ... def supports_stream(self) -> bool: ... async def stream(self, symbol) -> AsyncIterator[Bar]: ... # yields 1m bars ``` - `YahooSource` — real `history()`. `stream()` is a **polling loop** (every 15–30 s, `interval=1m&range=1d`, emit only bars newer than the last emitted) that presents the same async-iterator interface as a real push stream. - `SchwabSource` — `supports_history() -> False`. `stream()` is the true `CHART_FUTURES` websocket. - `ReplaySource` — reads a JSONL tape. Used by tests and offline development. Nothing downstream of these may know which source it is using. Selection is one env var. **In production both run at once:** Yahoo seeds history at startup, Schwab provides the live tail. ### Do not use Yahoo's daily bars Yahoo anchors `ES=F` daily bars to **midnight ET**, but the CME futures session runs **18:00 → 17:00 ET** (§6). Mixing the two definitions yields daily candles that disagree with every other chart you'll compare against. **Build daily bars yourself by aggregating Yahoo's 1h bars** through the same `aggregator.py` used for live data — one session definition everywhere. Yahoo's 1h bars are anchored to the top of the ET hour, so they compose into session-anchored 1d buckets cleanly. The ~730-day 1h window yields ~500 sessions: enough for a daily 200SMA (~200 sessions) with room to spare. Yahoo's native 1d bars may be used *only* for multi-year context, clearly labelled. ## 2.2 Schwab entitlements — **answered empirically 2026-08-10** All verified against a live production app holding both Market Data Production and Accounts and Trading Production. | Question | Answer | |---|---| | Futures market data entitled? | **Yes** — but only via `get_quotes()` (plural) | | Symbol format | **`/ES`**, which auto-resolves to the active contract `/ESU26` | | `CHART_FUTURES` streaming | **Works** — one true-OHLCV minute bar per symbol per minute | | `LEVEL_ONE_FUTURES` | **Works**, and reports `delayed: false` | | Futures price history | **Still none.** Yahoo remains the only source of the past | Three traps found the hard way, all of which cost a round trip: - **`get_quote()` (singular) silently returns the wrong instrument.** It puts the symbol in the URL *path*, where the leading slash is normalised away, so `/ES` comes back as `ES` — Eversource Energy, an equity, at $72. HTTP 200 with a populated body. `get_quotes()` passes symbols as a query parameter and returns the future correctly. **Never treat a 200 as proof; check `assetMainType`.** - **Streaming needs the Accounts and Trading product.** `StreamClient.login()` reads `/trader/v1/userPreference` for its socket URL, and that path is not in Market Data Production. A market-data-only app cannot stream at all. - **Authorisation codes expire in about thirty seconds**, and an unwritable token path spends one before revealing itself. `scripts/check_schwab.py` preflights the key, the secret and the token path for exactly this reason. Because `/ES` resolves to the active contract on Schwab's side, contract roll handling — an open problem in §10 — needs no code here. ### The remaining unknowns for Schwab 1. **Futures market-data entitlement.** It is not publicly documented whether `CHART_FUTURES` requires futures trading approval or a CME non-professional market data agreement on the Schwab account. Verify empirically in M6. 2. **Symbol format.** `schwab-py` docs show both `/ES` (continuous front-month) and `/ESZ25` (specific contract). Determine which the stream actually accepts, and whether the continuous form auto-rolls. Record the answer in the README. 3. **Bar cadence and lateness.** Confirm `CHART_FUTURES` emits one message per symbol per minute, whether it re-sends a bar (correction), and how bars behave across the 17:00–18:00 ET settlement break. 4. **Volume semantics.** Confirm `VOLUME` is per-minute, not cumulative-for-session. ### Confirmed facts (already verified — do not re-research) **Realtime futures data: fully available.** Do not let §10 below suggest otherwise — these are different axes and conflating them will send you down the wrong path. | Service | Available? | Use here | |---|---|---| | `CHART_FUTURES` (1-min OHLCV, `/ES`) | ✅ streaming | The base feed. Everything derives from it | | `LEVEL_ONE_FUTURES` (live quotes) | ✅ streaming | Current price/bid/ask for the status bar and alert evaluation | | `LEVEL_ONE_FUTURES_OPTIONS` | ✅ streaming | Live pricing of a proposed /ES put spread in M7 | | REST `get_price_history` for futures | ❌ **not available** | — see §10 | | `CHART_HISTORY_FUTURES` service | ❌ does not exist in `schwab-py` | — | | Futures / futures-options **order entry** | ❌ not offered | see §14 | - `ChartFuturesFields`: `SYMBOL=0, CHART_TIME_MILLIS=1, OPEN_PRICE=2, HIGH_PRICE=3, LOW_PRICE=4, CLOSE_PRICE=5, VOLUME=6`. - **The gap is historical only, not realtime.** schwab-py's docs, verbatim: *"Schwab provides price history for equities and ETFs. It does not provide price history for options, futures, or any other instruments."* A Schwab account does not change this — it is an API-surface limitation, not an entitlement one. Live `/ES` streams fine; there is simply no way to ask for *yesterday's* `/ES` bars. This is the single biggest constraint in the project; see [§10](#10-the-history-problem). - Lightweight Charts 5.2.0 standalone build exposes a `window.LightweightCharts` global containing `createChart`, `CandlestickSeries`, `LineSeries`, `createSeriesMarkers`, `LineStyle`. CDN: `https://unpkg.com/lightweight-charts@5.2.0/dist/lightweight-charts.standalone.production.js` (~196 KB). - **v5 changed the series API.** Use `chart.addSeries(LightweightCharts.CandlestickSeries, opts)`. The v4 `chart.addCandlestickSeries(opts)` form **does not exist in v5** — most tutorials online are v4 and will not work. --- ## 3. Architecture ``` YahooSource SchwabSource ReplaySource (history + poll) (live websocket) (JSONL tape) └──────────────────────┼──────────────────────┘ │ MarketDataSource protocol ▼ 1-minute OHLCV ┌─────────────────┐ │ StreamService │ single asyncio task, ONE connection │ (+ recorder) │──────► raw JSONL tape (for replay) └────────┬────────┘ │ Bar(1m) ▼ ┌─────────────────┐ │ Aggregator │ session-aware bucketing └────────┬────────┘ │ Bar(tf) closed / updated ┌──────────────┼──────────────┐ ▼ ▼ ▼ ┌──────────┐ ┌────────────┐ ┌──────────┐ │ BarStore │ │ Pivots → │ │ Moving │ │ (memory) │ │ Trendlines │ │ Averages │ └──────────┘ └─────┬──────┘ └────┬─────┘ │ │ └──────┬───────┘ ▼ Level[] (unified type) ┌──────────────────┐ │ ConfluenceEngine │ cluster + score └────────┬─────────┘ ▼ Cluster[] ┌──────────────────┐ │ AlertEngine │ state machine + cooldown └────────┬─────────┘ │ ┌──────────────┴───────────────┐ ▼ ▼ FastAPI WebSocket ntfy push │ ▼ Vue 3 + Lightweight Charts ``` ### Critical process constraint The Schwab stream is **one connection, one process, not thread-safe**. Therefore: - Run the streamer as a single `asyncio` task owned by FastAPI's `lifespan`. - **`uvicorn --workers 1` always.** More than one worker means more than one Schwab connection, which will fight over the session. - `--reload` in dev will tear down and re-establish the stream on every file save. That is acceptable locally but expect reconnect churn. - If the VPS deploy later needs multiple web workers, the streamer must be split into its own process with a message bus. Do not design for that now, but keep `StreamService` free of any FastAPI imports so the split stays cheap. --- ## 4. Module layout ``` main.py FastAPI app: lifespan, route mounting (exists, extend) app/ config.py Settings from env (pydantic-settings) auth.py Shared-secret gate (no-op when unset) market/ base.py MarketDataSource protocol, Bar emission contract yahoo.py YahooSource: history() + polled stream() ← build first schwab.py SchwabSource: easy_client, CHART_FUTURES websocket replay.py ReplaySource: JSONL tape stream.py StreamService: owns a source, reconnect, emit Bar recorder.py Record raw messages to JSONL bars/ models.py Bar, Timeframe session.py CME session calendar + bucket boundary math aggregator.py 1m -> all timeframes store.py BarStore protocol + InMemoryBarStore analysis/ indicators.py ATR, SMA, EMA (pure functions over bar lists) manual_lines.py Hand-drawn lines: CRUD + JSON persistence ← M5 pivots.py Fractal swing detection ← M8, deferred trendlines.py Candidate generation, scoring, dedup ← M8, deferred moving_averages.py MTF MA levels levels.py Level type + registry, rebuild orchestration confluence.py Clustering + scoring alerts.py State machine, cooldown, dispatch notify/ ntfy.py Phone push api/ routes.py REST ws.py WebSocket hub static/ index.html Vue 3 + LWC script tags (exists, replace) app.js Vue app root (exists, replace) chart.js Lightweight Charts wrapper (new) style.css (exists, extend) tests/ ... ``` --- ## 5. Data model Use dataclasses (or pydantic where it crosses the API boundary). All times are **epoch seconds, UTC**. Never store naive local datetimes. ```python class Timeframe(str, Enum): M1="1m"; M2="2m"; M5="5m"; M15="15m"; M30="30m"; H1="1h"; D1="1d" @property def seconds(self) -> int: ... # D1 is session-defined, not 86400 — see §6 @dataclass class Bar: tf: Timeframe t: int # epoch seconds, bucket OPEN time o: float; h: float; l: float; c: float v: int closed: bool # False while still forming symbol: str # e.g. "/ESZ25" — carried so contract rolls stay visible ``` `Level` is the unified abstraction that makes the whole design work. Trendlines and moving averages both reduce to "a price at time t, with a weight and a side". ```python class LevelKind(str, Enum): MANUAL="manual" # hand-drawn — ships first (M5) MA="ma" # moving average — ships first (M3) TRENDLINE="trendline" # auto-detected — deferred to M8 HORIZONTAL="horizontal" class Side(str, Enum): SUPPORT="support"; RESISTANCE="resistance" @dataclass class Level: id: str # stable hash — the UI diffs on this, so keep it stable kind: LevelKind tf: Timeframe side: Side weight: float # timeframe weight x quality multiplier score: float # raw quality score before weighting label: str # "4h resistance", "1h EMA21" # Geometry. Trendline: price(t) = slope*(t - anchor_t) + anchor_p anchor_t: int anchor_p: float slope: float # price units per SECOND. 0.0 for horizontal/MA-at-instant # For MAs: the stepped polyline actually drawn points: list[tuple[int, float]] | None touches: int first_t: int last_t: int provisional: bool # derived from a still-forming bar hidden: bool # layer-panel visibility — see §9.4 for its effect on scoring def price_at(self, t: int) -> float: return self.anchor_p + self.slope * (t - self.anchor_t) ``` The weight table referenced throughout — define it once, in `config.py`: ```python TIMEFRAME_WEIGHT = { Timeframe.M1: 1, Timeframe.M2: 1, Timeframe.M5: 1, Timeframe.M15: 2, Timeframe.M30: 3, Timeframe.H1: 4, Timeframe.D1: 16, } MA_WEIGHT_FACTOR = 0.75 # §7.4 — MAs weigh slightly less than drawn structure ``` ```python @dataclass class Cluster: id: str side: Side low: float; high: float; center: float score: float # sum of member weights members: list[Level] distance: float # signed points from current price ``` --- ## 6. Session and aggregation rules — read carefully This is where a naive implementation silently produces wrong lines. CME ES is not a 9:30–16:00 instrument. **Session definition (America/New_York, DST-aware via `zoneinfo`):** - Trading week opens **Sunday 18:00 ET**. - Daily settlement break **17:00–18:00 ET, Monday–Thursday**. No bars expected. - Week closes **Friday 17:00 ET**. - A **futures "day"** runs 18:00 ET → 17:00 ET the following calendar day, and is conventionally labelled with the *following* calendar date. Sunday 18:00 bars belong to Monday's daily bar. **Bucketing rules:** - `1m, 2m, 5m, 15m, 30m, 1h` — bucket on wall-clock UTC boundaries. These divide the hour evenly, so session anchoring is unnecessary and UTC keeps it simple. - `1d` — one bar per futures session as defined above. **This is the only session-anchored timeframe.** 4h used to be the other one and was the reason this section warned about DST; with 4h gone, that whole class of edge case went with it. Implement this as `session.py::bucket_start(t: int, tf: Timeframe) -> int` and unit test it hard, including both DST transitions and the Sunday open. **This function is the highest-risk piece of pure logic in the project.** Write its tests first. **Aggregator behaviour:** - Maintain one forming bar per timeframe. On each incoming 1m bar: - if `bucket_start(bar.t, tf)` differs from the forming bar's `t`, close the forming bar (emit `closed=True`), then open a new one; - otherwise fold in: `h=max`, `l=min`, `c=close`, `v+=`, emit `closed=False`. - **Gaps do not close bars by time — they close by the arrival of a later bar.** Never use a wall-clock timer to close a bucket; the market halts and holidays will fire it incorrectly. Exception: emit a `closed=True` for the previous bucket when a bar arrives that skips buckets entirely. - Aggregation must be **deterministic and replayable**: feeding the same 1m sequence twice must produce byte-identical output. No `datetime.now()` inside the aggregator. --- ## 7. Analysis engines ### 7.1 Indicators (`indicators.py`) Pure functions, list-in/list-out, no state: `sma(values, period)`, `ema(values, period)`, `atr(bars, period=14)`. ATR is the universal scale unit — every tolerance in this project is expressed in ATR multiples, never in fixed points, so the same config works whether ES is at 4,000 or 8,000. ### 7.2 Pivot detection (`pivots.py`) Fractal method, not regression. Regression fits the *middle* of price action; humans draw lines across *extremes*, and extremes are what other traders react to. ``` ● pivot high (w bars lower on both sides) / \ / \ ● / \ / \ / \ ───●───●────────────── pivot low ``` - `pivot_high(bars, w)`: index `i` qualifies if `high[i] >= max(high[i-w : i+w+1])` and `i` is the leftmost such index in ties. - Default `w = 3` for lower TFs, `w = 2` for `4h`/`1d` (fewer bars available). - **Confirmation lag is `w` bars — this is intentional.** A pivot is only known `w` bars after it forms. Do not "detect" pivots on the forming bar; that repaints, and a repainting line is worse than no line. - Significance filter: keep a pivot only if its prominence (distance to the surrounding swing in the opposite direction) `>= 0.5 * ATR(14)` on that timeframe. Drops noise pivots without hardcoding point values. ### 7.3 Automatic trendlines (`trendlines.py`) — **deferred to M8** > Not part of the initial build. **Manual trendlines (§7.3a) ship first.** This section > is retained because it is the eventual target and because §7.3a is deliberately > designed to produce the same `Level` objects, so adopting this later changes nothing > downstream. Skip to §7.3a on a first pass. ``` for each timeframe, for each side (highs → resistance, lows → support): P = last N qualifying pivots (N = 25; ~300 candidate pairs, trivial) for each pair (a, b) in P where a.t < b.t: line = through (a.t, a.p) and (b.t, b.p) evaluate(line) -> score or reject dedup, keep top K = 4 per side per timeframe ``` **Evaluation of a candidate line:** Let `tol = 0.25 * ATR(14)` on that timeframe. - **Violation** — a bar *closes* beyond the line by more than `tol` (above for resistance, below for support). Wicks do not count as violations; wicks through a level are normal and often the point. - **Touch** — a bar's extreme comes within `tol` of the line without violating it. - **Reject** the line if `violations > 1` between the two anchors, or if any violation occurred after the later anchor (the line is broken and no longer active). **Score:** ``` score = 3.0 * touches + 1.5 * log1p(span_in_bars) + 2.0 * recency_decay(last_touch) # exp(-age_bars / halflife), halflife=50 - 4.0 * violations ``` Normalize within each timeframe/side group so weights stay comparable across timeframes regardless of how many candidates a given timeframe happened to produce: ```python best = max(l.score for l in group) # after dedup, before truncation to K quality = clamp(l.score / best, 0.0, 1.0) if best > 0 else 0.0 level.weight = quality * TIMEFRAME_WEIGHT[tf] ``` So the strongest 4h line contributes the full 8.0, a mediocre one proportionally less, and a 5m line can never outweigh a 4h line no matter how many touches it has. **The four coefficients above (3.0 / 1.5 / 2.0 / 4.0) are starting values, not derived constants.** They cannot be got right on paper — expect to tune them by eye against replayed tapes in M4. Put them in `config.py`, not inline, and treat "the lines land where a human would draw them" as the acceptance criterion. **Dedup:** two lines are duplicates if, evaluated at *now*, their prices are within `tol` **and** their slopes differ by less than 20%. Keep the higher score. Without this you get a fan of ten nearly-identical lines from the same swing. **Recompute policy:** only on a **bar close** for that timeframe, never on every tick. A 4h line recomputes 6× per day. This is what keeps the whole thing cheap. ### 7.3a Manual trendlines (`manual_lines.py`) — ships in M5 A hand-drawn line is just a `Level` with `kind=MANUAL`. It flows into confluence, projection, and alerts through exactly the same path as everything else — that is what makes deferring the automatic engine cheap rather than a detour. **Why this ordering is better than it looks:** hand-drawn lines are *correct by definition* (you drew them). So they validate the confluence engine without the automatic detector's tuning risk, and they later become the ground truth that M8's scoring coefficients get tuned against. **Geometry.** Anchors are stored in **absolute epoch seconds and price** — never bar indices. This is why a line drawn on the 4h chart renders correctly on the 1m chart with no conversion: both are the same `(time, price)` plane. The existing `Level.price_at(t)` already handles it. **Timeframe attribution.** Tag the line with the timeframe that was *displayed when it was drawn*. A line drawn on the 4h chart is a 4h line and carries weight 8. This is the single most important field — without it every manual line would score identically. **Weight.** `quality = 1.0` always. The user drew it; it is not a candidate to be scored. `weight = TIMEFRAME_WEIGHT[tf]`. **Drawing interaction** (all APIs verified present in LWC 5.2.0): | Action | Implementation | |---|---| | Enter draw mode | Toolbar button; changes cursor | | Place endpoints | `chart.subscribeClick(handler)` → two clicks | | Pixel → price | `series.coordinateToPrice(param.point.y)` | | Pixel → time | `chart.timeScale().coordinateToTime(param.point.x)` | | Render | `LineSeries` with 2 points, extended right (same as §9 auto lines) | | Select | Click within ~6px of a line — hit-test in price space via `price_at(t)` | | Delete | `Delete`/`Backspace` on selection, plus a button | | Edit | **Delete and redraw.** Endpoint dragging is real work — do not build it in M5 | **Snapping.** When placing an endpoint, snap to the nearest bar high/low within ~8px. Cheap to implement and it is the difference between a usable drawing tool and a frustrating one. Make it toggleable; snap to high for resistance, low for support, inferred from drag direction or nearest extreme. **Persistence — required in M5, not deferred.** A user who redraws their lines after every restart abandons the tool. This does *not* require the bar store or a database: write to a **JSON file** (`data/manual_lines.json`), loaded at startup. Bar persistence can stay deferred to M7; these two are unrelated decisions. ```json {"id":"ml_01H...","tf":"4h","side":"resistance","anchor_t":1754600000, "anchor_p":6412.5,"slope":-0.0000031,"created_at":1754700000,"note":"","hidden":false} ``` CRUD via `POST /api/lines`, `DELETE /api/lines/{id}`, `PATCH /api/lines/{id}`. On any change the server recomputes levels and broadcasts `{"type":"levels",...}` — the drawing client must render optimistically and then reconcile, not wait on the round trip. ### 7.4 Multi-timeframe moving averages (`moving_averages.py`) **Primary requirement: the daily MA set — SMA 10, 20, 50, 100, 200 — computed on daily bars and displayed on the 1d, 30m, and 1m charts, with the base timeframe switchable.** This is the headline use of the multi-timeframe projection: the 200DMA is a level people trade against, and on a 1-minute chart it should appear as a near-horizontal line that steps once per session. Do not compute a "200-period MA of 1-minute bars" for the 1m chart — that is a completely different and far less useful line. **The MA's period is always tied to the timeframe it was computed on, never to the chart being displayed.** ``` MA_SETS = { "1d": [("sma", 10), ("sma", 20), ("sma", 50), ("sma", 100), ("sma", 200)], # optional, off by default: "1h": [("ema", 9), ("ema", 21)], } BASE_TIMEFRAMES = ["1m", "30m", "1d"] # the switcher; others remain available ``` Config-driven, so adding a set is a config change, not code. Other timeframes' MAs stay supported by the same machinery but ship disabled — the daily set is what matters. **History check:** a 200DMA needs 200 sessions. Yahoo's 730-day 1h window yielded 17,387 bars ≈ 750 sessions, so all five daily MAs are warm from startup with roughly 3× margin. Verified, not assumed. **Expect small disagreements with thinkorswim/TradingView on the daily MAs.** We build daily bars on the CME session (18:00→17:00 ET, §6); other platforms sometimes anchor differently or use settlement prices. A one- or two-point difference in the 200DMA is this, not a bug. Keep the daily anchor configurable so it can be matched if it matters. The key rendering decision: an MA from a higher timeframe drawn on a 1-minute chart is a **step function**, held constant between higher-timeframe closes. ``` 1h EMA21 rendered on a 1m chart: ┌──────── ┌──┘ ← steps at each 1h close, NOT a smooth interpolation ┌──┘ ``` Interpolating between higher-TF closes would draw a line that was never true at the time it appears to have been true. Emit `points` as a stepped polyline and render with LWC's `lineType: LightweightCharts.LineType.WithSteps`. - The value from the **forming** higher-TF bar is emitted with `provisional=True` and rendered dashed. It *will* move until that bar closes — that is honest, not a bug. - An MA's **current value is a price level**, so it enters the confluence engine on equal footing with trendlines, at `0.75 × timeframe_weight` (MAs are slightly less reactive than drawn structure, but a 4h 200SMA is still a wall). - **Warm-up:** an MA needs `period` closed bars on its timeframe. A daily 200SMA needs 200 sessions. Until warm, emit nothing — never emit a partially-warmed MA. Yahoo seeding (§10) makes every MA up to a daily 200SMA warm from startup; without it, the higher-timeframe MAs stay cold for months. ### 7.5 Confluence (`confluence.py`) This is the actual product. Everything above exists to feed it. ``` 1. Collect every active Level, evaluate price_at(now). 2. Split by side relative to current price (levels above → resistance, below → support). A level's stored `side` is its structural nature; its *effective* side is positional. Use positional — a broken resistance acting as support is the interesting case, not an error. 3. Sort by price. Single-linkage cluster: extend the current cluster while the gap to the next level is <= clusterTol. clusterTol = 0.4 * ATR14(15m) 4. Cluster score = sum of member weights. 5. Emit clusters with >= 2 members OR score >= 8 (a lone 1d line matters by itself). ``` ``` 5m resistance 6404.25 weight 1 15m resistance 6405.00 weight 2 1h resistance 6403.75 weight 4 4h resistance 6404.50 weight 8 ↓ RESISTANCE CLUSTER 6403.75 – 6405.00 CONFLUENCE 15 ``` Recompute on every closed 1m bar, and on any level rebuild. ### 7.6 Alerts (`alerts.py`) The failure mode to design against is notification fatigue. A per-line alert makes the system useless within a day. Per-cluster state machine, keyed on `(side, round(center / clusterTol))` so a cluster keeps its identity as members drift: ``` ARMED ──price within alertTol of cluster──► FIRED ──► COOLDOWN ──┐ ▲ │ └────── price moves > 2*alertTol away AND cooldown elapsed ◄────┘ ``` - `alertTol = 0.5 * ATR14(15m)` - `cooldown = 15 minutes` - Minimum score threshold to fire: **configurable, starting value 6 — but this certainly needs recalibrating in M4.** With the daily MA set as the primary levels, each daily MA carries `0.75 × 16 = 12`, so *any two* of them near each other scores 24 and a threshold of 6 would fire constantly. Either raise the threshold well above 24, or damp the weight when several MAs from the same timeframe cluster (they are not independent evidence the way a 4h line and a 1h line are). Decide this against a replayed tape, not on paper. **Target: single-digit alerts per session.** - Re-arming requires *both* the price separation and the cooldown. Time alone lets a price oscillating on a level fire forever. - Fire on **closed 1m bars only**, not intra-bar ticks. Dispatch to: WebSocket (UI banner + sound) and ntfy. Payload: ``` BEARISH ZONE /ES 6404.25 Resistance confluence 15 @ 6403.75–6405.00 5m, 15m, 1h, 4h ``` --- ## 8. API contract Fix these shapes now; the frontend and backend are built against them. ### REST | Route | Returns | |---|---| | `GET /api/status` | `{stream: "connected"\|"disconnected"\|"replay", symbol, last_bar_t, bars_held: {tf: n}, warm: {tf: bool}}` | | `GET /api/bars?tf=5m&limit=500` | `{tf, bars: [{t,o,h,l,c,v,closed}]}` — oldest first | | `GET /api/levels?tf=all` | `{levels: [Level]}` | | `GET /api/confluence` | `{price, clusters: [Cluster]}` | | `GET /api/health` | existing | ### WebSocket `/ws` Client → server on connect: ```json {"type": "subscribe", "tf": "5m"} ``` Server → client: ```json {"type":"snapshot","tf":"5m","bars":[...],"levels":[...],"clusters":[...],"price":6404.25} {"type":"bar","tf":"5m","bar":{"t":1754700000,"o":6403.5,"h":6405.0,"l":6403.0,"c":6404.25,"v":812,"closed":false}} {"type":"levels","levels":[...]} {"type":"clusters","price":6404.25,"clusters":[...]} {"type":"alert","cluster":{...},"message":"..."} {"type":"status","stream":"disconnected"} ``` Rules: - Send `bar` on **every** update of the forming bar (that is the live chart) but batch `levels` — they only change on higher-TF closes. - Always send a full `snapshot` on connect and after any reconnect. The client must never try to reconcile a gap. - Levels are sent for **all** timeframes regardless of the displayed timeframe. That is the entire point: a 4h line drawn through a 1m chart. --- ## 9. Frontend `static/index.html` loads Vue 3 and Lightweight Charts as script tags — no bundler, matching the existing app. ```html ``` ### Vue + Lightweight Charts integration — the one real gotcha **Never put the chart or series objects in `ref()` or `reactive()`.** Vue's deep reactive proxy will wrap the library's internal objects, which breaks identity checks inside the library and destroys performance on every update. Use `shallowRef`, or better, a plain module-scoped variable / `markRaw`. ```js const chartApi = shallowRef(null); // ✅ // const chart = ref(null); // ❌ will appear to work, then misbehave ``` ### Logical indices address the whole chart, not your bar array **Never derive a viewport from `bars.length`.** A logical index addresses the chart's *shared* time scale — the union of the time points of every series on it — not the candle array. Any series whose points pre-date the candle window prepends to that scale and shifts every logical index by its count. ```js // ❌ off by however many points the other series contribute timeScale().setVisibleLogicalRange({ from: bars.length - 160, to: bars.length + 5 }); // ✅ an instant cannot be renumbered by a later series timeScale().setVisibleRange({ from: bars.at(-160).t, to: bars.at(-1).t + step * 5 }); ``` This is not theoretical — see the 2026-08-10 entry in §16. The daily MAs carry one point per daily bar (617 of them, back ~2 years). They are attached by `syncVisibleLevels()` *immediately after* `setBars()`, so a logical range that was correct when set silently slid 617 bars — about ten hours — into the past one tick later. The chart looked frozen while the socket was perfectly healthy. Structure: - `chart.js` — a plain, framework-free wrapper class owning the LWC instance: `create(el)`, `setBars()`, `updateBar()`, `syncLevels(levels)`, `destroy()`. - `app.js` — Vue app owning state (timeframe, connection status, clusters, alert log) and the WebSocket. Calls into the wrapper imperatively in `onMounted` / watchers. - Chart lifecycle in `onMounted`; `chart.remove()` in `onUnmounted`; a `ResizeObserver` driving `chart.applyOptions({width, height})`. ### v5 API usage ```js const chart = LightweightCharts.createChart(el, {...}); const candles = chart.addSeries(LightweightCharts.CandlestickSeries, {...}); const line = chart.addSeries(LightweightCharts.LineSeries, { lineType: LightweightCharts.LineType.WithSteps, // for MTF moving averages }); candles.setData(bars); // once candles.update(bar); // every tick — never re-call setData ``` ### Rendering levels `syncLevels()` must **diff by `Level.id`**, not clear-and-rebuild. Rebuilding every series on each update causes visible flicker and leaks series objects. - Trendline → a `LineSeries` with two points: `(anchor_t, anchor_p)` and `(now + rightExtension, price_at(now + rightExtension))`. Extend ~20% of the visible range into the future so the line is usable ahead of price. - MA → a `LineSeries` fed the stepped `points` array. - Colour **by timeframe** (one hue per TF, consistent everywhere including the confluence panel). Line width scales with timeframe weight. `provisional` levels render dashed via `lineStyle: LightweightCharts.LineStyle.Dashed`. ### 9.4 Layer panel (checkboxes) Ships with M3. Visibility control is load-bearing here, not decoration — five daily MAs plus manual lines plus any optional intraday MA sets stack up fast. ``` CHART [ 1m ] [ 30m ] [ 1d ] ← base timeframe switcher LAYERS ───────────────────────────────── ☑ Daily MAs ██ ☑ 10 ☑ 20 ☑ 50 ☑ 100 ☑ 200 ───────────────────────────────── ☐ 1h MAs ▓▓ ☐ EMA9 ☐ EMA21 ───────────────────────────────── ☑ Manual lines ░░ (M5) ☐ Auto trendlines (M8) ───────────────────────────────── ☐ Hidden levels still count toward confluence ``` - **The base timeframe switcher changes only the candles.** Every level stays on screen — a 200DMA is equally valid on a 1m chart. That is the entire premise of the product. - **The group checkbox is a master toggle** — unchecking "Daily MAs" hides all five at once; individual periods nest under it. - The colour swatch beside each timeframe is that timeframe's hue, used identically on the chart and in the confluence panel. One hue per timeframe, everywhere. - **Persist to `localStorage`.** These settings are pure UI preference and must survive reloads; they do not belong on the server. **Visibility vs. scoring — decide this explicitly.** Hiding a level defaults to *also* removing it from confluence scoring, because "I don't want to see this" almost always means "I don't care about this." The last checkbox decouples the two for anyone who wants a clean chart with full scoring. That default has an architectural consequence: **confluence is computed server-side, so the client's enabled set must reach the server.** Send it over the WebSocket on change: ```json {"type":"prefs", "base_tf":"1m", "enabled":{"ma":{"1d":[10,20,50,100,200]},"manual":true,"auto":false}, "hidden_levels_score":false} ``` Store it per connection. Single-user app — no need for anything more elaborate. ### Panels - **Drawing toolbar** (M5) — trendline tool, snap toggle, delete selection. - **Confluence panel** — clusters sorted by `|distance|`, each showing member timeframes, the zone range, and the score. This is the primary readout; give it more visual weight than the price itself. - **Status bar** — stream state, contract symbol, last bar age, which timeframes are warm. When the stream drops, the chart must *say so*, not quietly show stale candles. - **Alert log** — recent fires, most recent first. - **Timeframe selector** — switches the candle series only. Levels stay. Keep the existing dark/light CSS-variable scheme in `style.css`. --- ## 10. The history problem **Mostly solved by the Yahoo source (§2.1) — but read the caveats.** Schwab alone would leave the system knowing nothing before the moment it connects. Since the highest-weighted timeframes need the most history, Schwab-only cold start would mean waiting months for the parts of the product that matter most: | Timeframe | Usable after, Schwab-only | With Yahoo seeding | |---|---|---| | 5m, 15m | ~2–4 hours | immediate | | 30m, 1h | ~1–2 sessions | immediate | | 1d | months | immediate (~500 sessions) | | 1d 200SMA | ~10 months | immediate | **Startup sequence:** 1. Seed from Yahoo: `interval=1h&range=730d`, plus `interval=1m&range=8d` for the fine detail near the present. 2. Aggregate through `aggregator.py` into every timeframe, using our own session rules. 3. Attach the live source (Yahoo poll, or Schwab once keys exist) and continue forward. 4. Persist everything (M7) so subsequent restarts need less seeding. **Caveats that must be honoured in code:** - **Tag every bar with its `source`** (`"yahoo"` / `"schwab"` / `"replay"`), and expose the seam in the UI. Silently blending delayed continuous data with live per-contract data produces trendlines nobody else can see. - **Roll gaps.** Yahoo `ES=F` is a continuous front-month series; quarterly contract rolls leave price discontinuities that will read as a trendline break or generate a bogus pivot. For M0–M5 this is acceptable. If it proves noisy, the fix is either panama-adjusting the seeded series or dropping pivots within ±1 bar of a known roll date (third Friday of Mar/Jun/Sep/Dec). Do not build roll adjustment pre-emptively. - **The 1m request cap is 8 days.** Longer 1m ranges must be fetched in ≤8-day windows and stitched. In practice you don't need deep 1m history — seed 1h and let the aggregator do the rest. - **Never mix Yahoo daily bars in** — see §2.1. If per-contract accuracy ever matters more than convenience, **Databento** sells proper CME history with real roll handling. Not needed now. --- ## 11. Configuration All via env, read in `config.py`. Ship a `.env.example`; `.env` is already gitignored. ``` # --- data sources --- LIVE_SOURCE=yahoo # yahoo | schwab | replay SEED_SOURCE=yahoo # yahoo | none YAHOO_SYMBOL=ES=F YAHOO_POLL_SECONDS=20 SEED_1H_RANGE=730d SEED_1M_RANGE=8d # 8d is Yahoo's hard per-request cap # --- schwab (only needed once LIVE_SOURCE=schwab) --- SCHWAB_API_KEY= SCHWAB_APP_SECRET= SCHWAB_CALLBACK_URL=https://127.0.0.1:8182 SCHWAB_TOKEN_PATH=./.schwab_token.json SCHWAB_ACCOUNT_ID= SCHWAB_SYMBOL=/ES # --- timeframes & indicators --- TIMEFRAMES=1m,2m,5m,15m,30m,1h,1d BASE_TIMEFRAMES=1m,30m,1d # the chart switcher MAX_BARS_PER_TF=5000 # in-memory ring buffer bound # Daily MA set is the primary requirement; others ship disabled. See §7.4 MA_SETS__1D=sma10,sma20,sma50,sma100,sma200 MA_SETS__4H= MA_SETS__1H= DAILY_ANCHOR_ET=18:00 # CME session open; change to match another platform MANUAL_LINES_PATH=./data/manual_lines.json CONFLUENCE_MIN_SCORE=6 # MUST be recalibrated in M4 — see §7.6 ALERT_COOLDOWN_SECONDS=900 NTFY_TOPIC= NTFY_SERVER=https://ntfy.sh CHART_AUTH_TOKEN= # empty = auth disabled (local dev) REPLAY_FILE= # set to replay a tape instead of connecting ``` **Token persistence:** `schwab-py`'s refresh token expires every **7 days** and re-auth is an interactive browser flow. Locally, keep `.schwab_token.json` out of the repo (add to `.gitignore`). On the VPS later, it **must** live on a Coolify persistent volume or every rebuild logs you out. Same for the eventual SQLite file. --- ## 12. Testing The market is closed most of the time you will be working. Build for that. **Record/replay is a milestone-1 deliverable, not a nicety.** `recorder.py` writes every raw stream message to JSONL with its arrival timestamp; replay feeds them back through the identical code path, either at wall-clock speed or as fast as possible. Everything downstream of `StreamService` is then testable, deterministically, offline. Required tests: - `session.py` — bucket boundaries. Both DST transitions, Sunday 18:00 open, the 17:00–18:00 break, and Friday close. **Write these first.** - `aggregator.py` — 1m→all TFs on synthetic bars; gap handling; idempotent replay. - `moving_averages.py` — a known daily series produces known 10/20/50/100/200 values; assert nothing is emitted before warm-up; assert the stepped projection onto 1m holds its value for a whole session and changes exactly at the session boundary. - `manual_lines.py` — round-trip JSON persistence; a line drawn on 4h evaluates to the same price on the 1m chart at the same instant. - *(M8)* `pivots.py` — known fixtures; assert no repainting (a pivot, once emitted, never changes when more bars arrive). - *(M8)* `trendlines.py` — hand-built fixtures where the correct line is obvious; assert violation rejection and dedup. - `confluence.py` — synthetic levels producing a known cluster and score. - `alerts.py` — assert no re-fire within cooldown, and that oscillation around a level produces exactly one alert. Add `pytest` to a `requirements-dev.txt`. --- ## 13. Milestones Ordered so the user's stated priority — **live realtime charts first** — lands earliest, and so nothing later is blocked on market hours. **No API keys are required until M6.** M0–M5 run entirely on Yahoo. ### M0 — Yahoo source (no keys, no blockers) `market/base.py` protocol + `market/yahoo.py`: `history()` over the verified chart endpoint, and `stream()` as a polling loop presenting the same async-iterator interface. Null-filtering, 8-day 1m windowing, bar `source` tagging. Recorder writes a tape; `ReplaySource` reads it back. **Done when:** a script prints seeded 1h bars back to 2024 and then live-ish 1m bars, and a recorded tape replays identically. ### M1 — Live chart end to end ⭐ primary deliverable `StreamService` → in-memory 1m store → WebSocket → Vue 3 + LWC candlestick chart updating live. Status bar showing source + bar age. No analysis yet. **Done when:** the browser shows a live-updating /ES 1-minute candle chart, and the same chart can be reproduced offline from a tape. ### M2 — Aggregation + timeframe switching `session.py` + `aggregator.py` with full test suite. Timeframe selector drives the candle series. `GET /api/bars`. **Done when:** switching to 15m shows correctly bucketed bars, and replaying a tape twice yields identical output. ### M3 — Multi-timeframe moving averages ⭐ start here for analysis MAs are the right first analysis layer: **fully deterministic, no parameters to tune, no judgment calls**, and Yahoo seeding makes them warm from startup. They validate the entire overlay concept — stepped rendering, level diffing by `id`, TF colour scheme — without any of the ambiguity trendlines carry. **Done when:** the 10/20/50/100/200 DMAs render on the 1m, 30m and 1d charts, stepping once per session on the intraday views, dashed while the current session is unfinished. ### M3.5 — Layer panel Checkbox tree to show/hide levels by timeframe and by individual MA (§9.4). Ships with M3 because 6 timeframes × 4 MAs = 24 lines is unreadable without it. **Done when:** unchecking a group removes its every level from the chart and the state survives a reload. ### M4 — Confluence + alerts (on moving averages alone) ⭐ first genuinely useful build `confluence.py`, `alerts.py`, confluence panel, ntfy push, in-browser sound — scored over MA levels only. Multi-timeframe MA confluence is a real signal in its own right; this is a complete, useful product with zero hand-drawn input and zero tuning. **Done when:** a replayed tape produces a sane number of alerts (single digits per session), each corresponding to a real multi-timeframe convergence. ### M5 — Manual trendlines Two-click drawing, snapping, persistence, feeding the same confluence engine (§7.3a). Hand-drawn lines are authoritative — full weight, no quality discount. **Done when:** a line drawn on the 4h chart appears correctly projected on the 1m chart and raises the confluence score of a cluster it lands in. ### M6 — Schwab live source (needs API keys) Implement `market/schwab.py` against the existing `MarketDataSource` protocol and answer every question in [§2.2](#22-verify-these-when-adding-the-schwab-source-m6-not-before). Yahoo continues to handle seeding; Schwab takes over the live tail. **Done when:** flipping `LIVE_SOURCE=yahoo` → `schwab` changes nothing visible except lower latency and true per-contract prices. **If this milestone requires touching any file outside `market/`, the abstraction in M0 was wrong — fix it there, not here.** ### M7 — Persistence `SqliteBarStore` behind the existing `BarStore` protocol. Backfill-on-start from disk, falling back to Yahoo seeding only for what's missing. **Done when:** restarting the process loses no history. ### M8 — Automatic trendline detection (optional) `pivots.py`, `trendlines.py` per §7.3. Deliberately last among the analysis work: by this point the hand-drawn lines from M5 are **ground truth**, so the scoring coefficients can be tuned to agree with lines you actually drew, rather than guessed at in the abstract. Auto lines render in a distinct style and are individually dismissable; they never silently replace a manual line. **Done when:** on a replayed tape, auto-detected lines land where the manual ones were drawn, and the layer panel can hide them independently. ### M9 — Bias panel BULLISH / BEARISH toggle recording the user's directional call against the current confluence state, persisted, with a journal view. **Records only — trades nothing.** Because `LEVEL_ONE_FUTURES_OPTIONS` streams, this milestone can go further than the original sketch: given a bias, resolve the 1-DTE strikes, subscribe to the two legs, and display the **live spread price** — so the readout becomes actionable enough to hand-execute in thinkorswim: ``` BEARISH /ES — resistance confluence 15 @ 6403.75–6405.00 Proposed: 1-DTE 6405/6415 put spread ~2.35 x 10 (live) ``` Strike/expiration symbol resolution for futures options is fiddly; treat it as its own sub-task and verify the symbol format against `LEVEL_ONE_FUTURES_OPTIONS` empirically, the same way M6 verifies `/ES`. ### M10 — VPS deploy (when wanted) Shared-secret auth on, `workers=1`, persistent volume for token + DB, Coolify domain port suffix preserved per README. --- ## 14. Why execution is out of scope Note the asymmetry: Schwab **does** stream futures-options *quotes* (`LEVEL_ONE_FUTURES_OPTIONS`), so we can price a spread live — we just cannot transmit it. Read anything below as being about order entry only. Schwab's Trader API exposes no futures or futures-options **order entry**. thinkScript `AddOrder()` places *simulated* orders for backtesting only. Automating clicks in the thinkorswim UI is the wrong reliability model for near-expiration leveraged instruments — window focus, stale quotes, partial fills, and dialogs all fail silently, and an execution path must be able to tell the program what the broker actually did. The interim workflow is therefore: this app produces a decision, the human executes it in thinkorswim. Keep the seam clean. Broker-specific code lives only in `market/schwab.py`; nothing in `analysis/`, `bars/`, or `api/` may import it. When an execution adapter is added — Schwab, if they ever ship futures-options orders, or IBKR — it consumes `Cluster` and the M9 bias signal and nothing else. This is the same discipline the M6 acceptance test enforces for data sources. --- ## 15. Risk register | Risk | Impact | Mitigation | |---|---|---| | Futures entitlement missing on the Schwab account | Delays M6 only | No longer blocks — M0–M5 run on Yahoo | | No futures history from Schwab | Higher TFs cold for months | Solved: Yahoo seeding, §10 | | Yahoo endpoint is unofficial — may rate-limit or change shape | Dev source breaks | Isolated in `market/yahoo.py`; cache seeds to disk (M7) so it's fetched rarely; back off on 429 | | Yahoo daily bars anchored midnight ET, not session | Daily candles disagree with every other chart | Never use them — build 1d from 1h, §2.1 | | Contract roll gaps fake a trendline break | Bad signals on seeded data | Store `symbol` + `source` per bar; surface rolls in UI; §10 | | Source abstraction leaks Schwab/Yahoo specifics upward | M6 turns into a rewrite | M6 acceptance test: no file outside `market/` may change | | `session.py` bucket math wrong | Silently wrong lines everywhere | Tests written first; both DST transitions | | Repainting pivots | Lines that "were always there" | `w`-bar confirmation lag, enforced by test | | Alert fatigue | Product becomes unusable | Cluster-level alerts, cooldown + separation re-arm | | Multiple uvicorn workers | Duplicate Schwab connections | `workers=1`; streamer in `lifespan` | | Schwab token expiry (7 days) | Stream dies | Surface prominently in status bar; document re-auth | | Vue reactivity wrapping chart objects | Perf collapse, odd bugs | `shallowRef`/`markRaw` — §9 | | LWC v4 tutorials copied | Code silently wrong for v5 | `addSeries(SeriesType, ...)` only | | Viewport derived from `bars.length` | Chart looks frozen; feed is fine | Anchor the view by time, never by logical index — §9 | | Seed replays every bar through `on_bar` | ~82 s startup; port refuses connections | Known, unfixed — §16, 2026-08-10 | | Headless browser without a real locale | `Intl` throws; blank canvas mimics an app bug | Launch Chromium with `--lang=en-US` — §16 | ## 16. Session log Dated record of problems hit and how they were resolved. Times are UTC; the repo's commit timestamps are -0500. ### 2026-08-10 — rebuild, and a chart that looked frozen **10:30 · The rebuild was genuinely required.** `schwab-py` had been added to `requirements.txt`, but the running image was built at 2026-08-09 22:05, before that line existed. The bind mount (`.:/app`) hides this: source edits appear live, so the Schwab commits looked deployed while `pip freeze` in the container showed no `schwab-py` at all. Anything imported rather than read from disk needs `docker compose build`. Rebuilt to `schwab-py 1.5.1` and recreated the container. **10:30–10:31 · Startup takes ~82 seconds, and the port is closed the whole time.** `Runtime.start()` replays every seeded bar through `on_bar`, and each daily-bar update re-runs `rebuild_levels()` → `broadcast_level_delta()`, which serialises and diffs five MA levels carrying ~730 points each. With a 730d/1h seed plus an 8d/1m seed that is quadratic work before uvicorn binds. Measured: 10:30:24 "Waiting for application startup" → 10:31:46 "Application startup complete". An open browser tab polling `/api/status` throughout logs a wall of `ERR_CONNECTION_REFUSED`; that is the restart window, not a fault. *Unfixed.* The fix is to bulk-load seeded bars and rebuild levels once at the end, rather than once per bar. Related: M7 persistence would cut the seed itself. **Diagnosing a hang that is actually slowness:** `docker stats` reported ~0.1% CPU while the process was in fact grinding, so it pointed the wrong way. What worked was `faulthandler.dump_traceback_later(25, exit=True)`, which named the exact frame (`indicators.py:sma` under `runtime.py:62`). `py-spy` is unusable here — it needs `SYS_PTRACE`, which the container does not have. **Do not write scratch files into the repo while diagnosing.** A `_probe.py` dropped in the project root is inside the bind mount, so `--reload` restarted the lifespan and reset the 82-second clock — twice — which is what made slow startup look like an infinite hang. Pipe throwaway scripts over stdin (`docker exec -i … python -`) instead. Only `.py` changes trigger the reloader; writing screenshots into `artifacts/` is safe. **10:35 · A blank chart canvas that was not a bug.** The Playwright container has no usable locale, so Chromium reports `en-US@posix`; Lightweight Charts formats its time axis through `Intl`, which throws `Invalid language tag` and leaves the canvas empty. `docker-compose.yml` already sets `LANG=en_US.UTF-8` for that service and it is *not* sufficient. Launch with `chromium.launch({ args: ['--lang=en-US'] })` — with that, the page renders and reports zero console errors. Worth stating plainly: this failure looks exactly like a broken app, and it is not. **10:41–10:50 · The real bug — the chart sat ~10 hours behind a healthy feed.** Symptom: header price live at 7785.00 while the last candle closed 7772.75, and the series appeared to end at 00:20. Everything downstream checked out — `/api/bars` newest 10:39 from `schwab`; `store.put` keeps bars strictly ascending; the WebSocket snapshot delivered 1000 ascending bars ending 10:42 and live `bar` events arrived every minute; the browser received all of it. Interrogating `window.__chart` gave the answer: ``` seriesLen 1000 seriesLast 08-10T10:48 (7786.25) ← data complete visible 08-07T20:41 → 08-10T00:35 ← viewport wrong logical from 840 to 1005 ``` The series was complete; the *viewport* was 617 bars too far left — exactly `bars_held.1d`. `setBars()` set a visible **logical** range from the candle array length, then `syncVisibleLevels()` attached the daily MA series, whose 617 daily points pre-date the 1m window; prepending them renumbered every logical index and dragged the view off the live edge. Fixed in `static/chart.js` by anchoring the viewport to a **time** range. Verified in a real browser: visible range 08:02 → 10:49, last candle 7786.25 matching the header. See §9. **Method note.** Three checks in a row said "healthy" — the REST API, the WebSocket, and the frontend source all looked correct in isolation, because each of them *was* correct. Only querying the live page's own chart object separated "the data is missing" from "the data is off-screen". Screenshots alone were actively misleading here: the stale time axis was read as a session gap. ### 2026-08-10 (later) — real-time ticks, and what to do about cold restarts **The chart now moves between minute closes.** `CHART_FUTURES` emits a bar only once its minute is over, so the chart stepped once a minute and sat still in between — read, reasonably, as a dead feed. `LEVEL_ONE_FUTURES` carries real trades on the same socket (`delayed: False`, verified on this account back in M6), and it was never subscribed. It is now, and it builds a forming bar for the current minute which the authoritative `CHART_FUTURES` bar then supersedes. Three constraints shaped it, each of which would have caused a real bug: - **Tick bars must never reach the aggregator.** It accumulates with `current.v += incoming.v`, so re-sending the same forming minute would add its volume into every higher timeframe on every update. `Runtime.on_bar` returns early for `not bar.closed`: store the bar, set the price, broadcast, stop. - **Ticks are throttled** (`SCHWAB_TICK_SECONDS`, default 1.0). /ES trades many times a second and each emission costs a store write plus a broadcast to every open socket. Setting it negative drops the Level 1 subscription entirely and returns the source to closed bars only. - **A tick for a minute already closed is dropped**, or a late trade would overwrite a settled exchange bar with a partial one. Alerts deliberately stay on closed bars. A level is judged on a settled bar, not on a price that may not last the minute — and `on_bar` already gated on `closed`, so this needed no change. Intra-bar alerting is a separate decision. Bid-only Level 1 updates are skipped rather than carried forward: a bid is not a trade and must not extend a candle's high or low. Verified live — 15 forming bars and 2 closed bars in 100 seconds, and in a browser the candle's high and low visibly extend within the minute. **Cold restarts — the options, and a recommendation.** Every restart costs ~82 seconds of refused connections, re-seeds from Yahoo, and starts with empty alert cooldowns, so a deploy can re-alert whatever price is sitting on. 1. *Make seeding non-quadratic.* Seeding replays every bar through `on_bar`, and each daily-bar update rebuilds all five MA levels and diffs them. Bulk-load the seeded bars and rebuild levels once at the end. Contained, testable, and removes most of the 82 seconds. **Do this first** — it is the cheapest real win and needs no new storage. 2. *Persist bars (M7, SQLite).* Restarts then seed only the gap. Removes the Yahoo dependency from the startup path and shrinks the window further. This is the durable answer, and the plan already scopes it. 3. *Persist alert cooldowns and armed state.* Independent of 1 and 2, and the part that actually misbehaves rather than merely being slow: without it every deploy re-alerts. Small table, big behavioural win. 4. *Serve before seeding finishes.* Start uvicorn immediately and seed in a background task, so the port never refuses. The chart would open cold and fill in, which is better than an unreachable page — but it changes what "warm" means to every consumer of `/api/status`, so it wants its own thought. Recommended order: 1, then 3, then 2. 4 only if the window still bites after 1. **Stale bar events across a timeframe switch.** `Cannot update oldest data` appeared in the console once ticks were live. Switching timeframe races: the server answers `subscribe` with a fresh snapshot from one coroutine while another is still draining bar events for the timeframe just left, so a 1m bar can land after the 1h snapshot. Applied to the 1h series it is older than every point in it, and Lightweight Charts throws rather than ignoring it — taking the app down instead of dropping one bar. The race predates the tick feed; Level 1 made bar events ~15x more frequent, which is what surfaced it. Guarded at both ends. `app.js` honours the `tf` the event already carries and drops anything for a timeframe that is no longer selected. `chart.js` refuses a bar older than the series' last point regardless of where it came from — a bar behind the last one has nothing to contribute. Verified: 36 rapid timeframe switches under a live tick feed produce zero errors, and calling `candles.update()` directly with a stale bar still throws while the guarded `updateBar()` does not. **Same-price trades were being dropped.** The candle still paused for 10–20 seconds at a time after Level 1 went in. Instrumenting the raw stream settled it: 87 messages in 90 seconds, only 33 carrying `LAST_PRICE`. Most of the rest are pure bid/ask movement and correctly ignored — but a seventh of them look like this: ``` ['ASK_SIZE','ASK_TIME_MILLIS','BID_SIZE','BID_TIME_MILLIS', 'LAST_SIZE','QUOTE_TIME_MILLIS','TOTAL_VOLUME','TRADE_TIME_MILLIS','key'] ``` Trade time, trade size, cumulative volume — and no `LAST_PRICE`, because Level 1 sends only *changed* fields and the trade printed at the price of the one before. Requiring `LAST_PRICE` threw those away along with their volume. `parse_level_one` now treats size-plus-trade-time as a trade and returns a null price for the caller to carry forward. Measured on the live feed: median gap 3.1s → 2.0s, worst 21.5s → 8.1s, and bar volume climbs within the minute instead of standing still. Worth recording for the next person who reads a gap as a bug: the remaining pauses are the market, not the pipe. In thin pre-open tape /ES genuinely goes seconds without a price-changing trade, and then moves several ticks at once — which is what a "gap up" after a quiet spell actually is. **The time axis reads local, the data stays UTC.** Lightweight Charts is timezone-agnostic: it reads epoch seconds as UTC and labels them as UTC, which is why the axis disagreed with the wall clock. Fixed with `tickMarkFormatter` for the axis and `localization.timeFormatter` for the crosshair, both going through the browser's own zone. Deliberately *not* fixed by shifting the bar timestamps, which is the other common recipe. Every time in this codebase is epoch UTC by convention, and the chart's own times feed trendline anchors, `indexAt`, hit testing and the values posted back for manual lines — an offset applied to the data would put all of them out by the offset, which is exactly the class of bug that once priced a trendline 147 points away. One limit worth knowing: tick *placement* is still computed on UTC days, so the day-change divider sits at 00:00 UTC rather than local midnight, labelled with the local date. The labels are right; the divider is in the UTC place. ### 2026-08-10 (afternoon) — update rate, the left scale, and volume **Schwab conflates Level 1 to one update per second.** Chasing "still slow in market hours" ended at a hard ceiling rather than a bug. In regular hours the gaps between updates are whole multiples of 1.005s — 2.01, 3.02, 4.03 — which only happens if the source emits on a one-second cadence and some seconds carry no trade. `SCHWAB_TICK_SECONDS` was the limiter at 1.0 and is now 0.25, where it no longer binds. **One update per second is the source's ceiling.** Anything faster would mean inventing prices between trades, which a chart must not do. Two real losses were found on the way and fixed: - Higher timeframes only moved once a minute, because tick bars are 1m and the socket filters by subscriber timeframe. `Runtime.provisional_higher` now combines the aggregator's committed state with the live minute — without mutating it, since the aggregator accumulates volume and would double count. - Trades carrying only a trade stamp and a moved `TOTAL_VOLUME` — no `LAST_PRICE`, no `LAST_SIZE` — were skipped. 66 → 74 updates per 90s. **Daily context moved to the left price scale.** The right had prior-day levels, session VWAP and five daily MAs competing with the live price and hand-drawn intraday levels. The trap: a price scale takes its range from the series on it, so moving levels across draws them against a different range and puts them at the wrong height. A transparent candlestick mirror on the left scale feeds it exactly the right scale's input; verified as a zero-pixel delta between the two. Hand-drawn levels stay right, which is the space being cleared. `priceScaleId` is fixed at series creation, so it is passed at construction and kept out of the options reapplied afterwards. **Volume is finally drawn.** It travelled the entire pipeline — parsed from both Schwab services, aggregated, stored, broadcast in every bar — and nothing rendered it. Now an overlay histogram on its own hidden scale in the bottom fifth. An overlay rather than a pane, and emphatically not the price scale: volumes are five figures against four-figure prices, and sharing a scale would flatten the candles to a line. **Sidebar vertical space.** Three cuts, all in §9.4's layer panel. The daily MA periods sit on one line — `flex-wrap:nowrap` with tighter gaps and 12px boxes, where 10px gaps and 22px indent had pushed 200 onto a line of its own. Auto trendlines joins Manual lines as a parenthetical `(auto)` rather than owning a row, which suits a control that is disabled until M8. The alert log becomes a `
` like Confluence zones, closed by default with its count in the summary — collapsed by default is the point, since leaving it open would save nothing, and the count means activity is still visible while closed. **Sidebar vertical space.** The right column was taller than the viewport with nothing selected. Five changes, no functionality removed: - The five daily MA periods fit one line (`flex-wrap:nowrap`, tighter gaps, 12px boxes); 10px gaps and a 22px indent had pushed 200 onto a row of its own. - Auto trendlines becomes a parenthetical `(auto)` on the Manual lines row rather than owning one, which suits a control disabled until M8. - Alert log becomes a `
` like Confluence zones, closed by default with its count in the summary so activity still shows while shut. - Tools becomes a `
` too, open by default, and each tool's panel is bound to `armedTool` — only the armed tool shows its label, colour, width and side controls. `armTool` already toggles and permits one armed tool at a time, so the panels follow it exactly. - Order is Layers, Tools, then the rest, with Layers collapsed by default. Measured with nothing armed: 1110px of content down to 900px, which is inside the viewport rather than past it. `.sidebar-section:first-of-type` carries the zeroed top margin so reordering cannot reintroduce a gap at the top. ### 2026-08-10 (evening) — chart comments, and Drawings **Comments are drawings, not levels.** A comment is stored as a `ManualLine` with `kind="comment"`, so it inherits persistence, the shared drawing-number sequence, the sidebar list, filtering and deletion without a parallel set of endpoints. The one rule that must never bend: `ManualLineStore.levels()` filters comments out. A comment reaching the level list would join a confluence cluster and push a phone notification about a piece of text. It is also created with `armed=False`, and `PATCH /lines/{id}` returns `to_dict()` rather than `to_level()` for one, so no caller is ever handed a level-shaped comment. `kind` is derived when absent — zero slope was always a typed level, anything else a drawn trendline — so drawings saved before comments existed keep working. **Pinned or floating.** Pinned comments carry `anchor_t`/`anchor_p` and move with the chart; floating ones carry `x`/`y` as fractions of the pane, hold their place through any zoom, and can be dragged. Comments render as DOM rather than canvas: they hold arbitrary text, collapse to a numbered dot, and a floating one has to ignore the time scale entirely. A pinned comment scrolled out of view parks on the edge it left, pointing back the way it went, so it is never simply lost. **"Lines & levels" becomes "Drawings"**, filtered by type and by text — the text match covers the label, the kind and the `#number`, so `comment`, `cpi` and `7` all narrow the list. Delete acts on what the filter shows, which is what makes deleting by type or by string a single button. One CSS trap worth recording: `.trendline-row span { grid-column:2 }` captured the comment row's icon span and dragged it into the text column. Scoped to `span:not(.drawing-icon)`. **A comment lost its place when the timeframe changed.** Placed on a 30m bar, then switched to 15m, it slid to the far left. `timeToCoordinate` answers only for times that are data points on the current series, so a 30m bucket start returned `null` on another timeframe — and `null` was being read as "off the left edge". Anchors are now resolved to the bar that *contains* them, which is timeframe-independent: an 09:30 note sits on the 09:30 bar at 15m and on the 09:00 bar at 1h. `setBars` also re-renders comments, since a timeframe switch replaces the grid underneath every pinned one. Verified across 30m → 15m → 1h → 30m: the anchor stays 08:30 throughout, resolving to the 08:30 bar on 15m and the 08:00 bar on 1h, never edge-parked, and returning to its original x on the way back. Edge-parking still works where it should — a comment scrolled 400 bars out parks right and comes back on return to live. **The trendline Side control became inert.** Once snapping always lands on a bar extreme, the side is inferred from *which* extreme — a high is resistance, a low is support — so the dropdown could no longer affect anything. It now appears only when "Snap to highs/lows" is off, which is the one case where there is no extreme to infer from; otherwise the row reads "Side auto". Verified both ways: snap on shows the note and no dropdown, snap off shows the dropdown. `created_at` (epoch seconds) is already stored on every drawing and returned by `GET /api/drawings`, so filtering by age needs UI only, not a migration. **Trendline placement, third pass — and a regression I shipped.** Making a pending anchor always win (previous entry) fixed the twitch case and broke the opposite one: a genuine press-drag begun after an abandoned click was hijacked by that stale anchor, so the line started far from the drag. That reached production. The rule is now a single threshold — 12px of travel between press and release makes it a drag, which is wide enough to survive a twitch on a deliberate click and unambiguous for a real drag. A drag clears any half-placed anchor rather than silently adopting it. **The crosshair was lying about the anchor.** Lightweight Charts defaults to `CrosshairMode.Magnet`, which snaps the crosshair to the bar's *close*. Hovering by a bar's low therefore drew the crosshair mid-bar, and a correctly-snapped anchor looked wrong — measured: aiming 4px above a bar low placed the anchor at the low (7773) and not the close (7773.25), while the crosshair sat at the close. Arming a tool now switches the crosshair to `Normal`, and a snap dot marks the exact point the anchor will use, coloured by the side it implies. Four gesture paths are verified in a browser: two clicks with a twitch on the second, an abandoned click followed by a real drag, a plain press-drag, and hovering. All start where they should and land on a bar extreme. Worth recording for diagnosis: a reported "line ended up high off the bar" turned out to render exactly on its bar — zero pixels off at 1h, 30m and 15m — because the anchor had snapped to the *drawn* timeframe extreme (the 09:00 1h low, 7744.25) while being checked against 1m bars, where it matches neither extreme. Always compare an anchor against the timeframe it was drawn on. **A zero price wrecked every timeframe's scale.** A LEVEL_ONE_FUTURES update arrived with `LAST_PRICE: 0`. The parser rejected `None` but `0` is not `None`, so a minute opened at zero — `o=0.0 h=7777.25 l=0.0` — and `provisional_higher` carried that low into 5m, 15m, 30m, 1h and the daily bar, flattening the price scale everywhere. Non-positive prices are now treated as absent, so the last real price carries forward, and the tick still counts as a trade. **The exchange's own bars were being dropped.** `store.put` replaced a bar only when it matched the *tail*. That held while one closed bar arrived per minute, but ticks open the next minute before CHART_FUTURES delivers the previous one — so the authoritative bar no longer matched the tail and was discarded, leaving the tick approximation and its partial volume in place permanently. `put` now searches back a bounded number of buckets, and refuses to let a provisional bar overwrite a settled one. Both were introduced by the tick feature and both are covered by tests: a zero price parses as a trade with no price, a late closed bar replaces its bucket and keeps the exchange's volume, and a tick cannot overwrite a settled bar. **Snapping now measures distance on screen, not in time.** The rule was "take the bar sharing the cursor's time, then its nearer extreme", which ignored how far that extreme actually was. Pointing anywhere below a candle snapped to that candle's low however distant, and the extreme genuinely under the cursor was never considered — so zoomed out to ~360 bars at three pixels each, hitting the intended bar took several attempts. `snapPoint` now scans six bars either side and picks the extreme nearest in pixels. Proven by probe: with the cursor on one bar's low but nudged two pixels so `coordinateToTime` resolves to its neighbour, the snap takes the extreme under the cursor rather than the neighbour's. Worth recording because it was misdiagnosed twice: a report of "the snap dot appears way above the bar" was, on the numbers, the dot landing correctly on the bar's low while the cursor sat 151 points below it. The right price scale keeps a `bottom: 0.1` margin and the volume overlay is drawn in it, so the lower fifth of the pane is below every candle — an inviting place to point that contains no price action at all. ### e2e tests `bin/e2e` runs `tests/e2e/*.test.mjs` inside the playwright service against the dev stack. Node's built-in test runner, no dependencies added to this repo: Playwright is global in that container and `tests/e2e` is mounted at `/repo/tests/e2e`. Every case in there is a bug that shipped — the viewport parked ten hours back, hourly candles drawn as slivers, stale bar events throwing, comments drifting on a timeframe switch, and three separate ways a trendline anchor could disagree with its own preview. None of them could have been caught by pytest, which is the argument for the suite existing. Tests clean up after themselves: `withChart` records the drawings that exist before the body runs and deletes anything new afterwards, because the dev store is shared with whoever is using the app. Select by title rather than class when asserting on chart overlays, for the same reason. ### The snapping rule, stated once **x picks the bar, y picks which extreme.** That is the whole rule. It is written here because changing it reactively three times is what made trendlines feel broken, not any inherent difficulty: 1. An 8px proximity gate meant a cursor between the high and the low snapped to neither, so the anchor kept a raw mid-bar price and the side silently fell back to the dropdown. 2. Removing the gate fixed that. Then a nearest-in-2D search was tried, to make a bar easier to hit when zoomed out — and broke sweeping along the bottom, because whichever nearby bar had the lowest low won on total distance and the dot skipped off the bar under the cursor. Reverted. 3. What actually made it feel wrong was never the rule: the crosshair was in Lightweight Charts' default Magnet mode, snapping to the bar's *close*, so the feedback pointed somewhere the anchor would never go. It is Normal everywhere now, with the snap dot showing the real target. An e2e test sweeps the cursor along the bottom of a zoomed-out 1h chart and requires every position to land on the low of the bar beneath it — 115 of 115. That test is the rule, executable. **The snap leapt to the live edge — found by diagnostic mode.** Reported from the user's own browser, which no headless run had reproduced: ``` cursor_x 1409.0 chart_w 1280.0 -> cursor_t None -> snapped to the last bar cursor_x 1161.0 chart_w 1280.0 -> cursor_t None -> snapped to the last bar cursor_x 1128.0 -> valid time, drift 0 bars ``` `coordinateToTime` answers `null` over the right-hand price axis, over the whitespace past the last bar, and anywhere outside the chart — and `snapPoint` read that as "the newest bar", so the dot jumped to the live edge from wherever the cursor was. Two faults behind it: the tool's pointer listener is on `window` and therefore fires over the sidebar (x=1409 on a 1280-wide chart), and a null time meant a default rather than no answer. Now a pointer outside the plot hides the indicator entirely, and a null time resolves to the bar nearest in *pixels* rather than the newest one. Covered by an e2e test that hovers a bar, the axis, the sidebar, and back. The lesson is about method rather than geometry: four hypotheses were tested and killed by measurement here — device pixel ratio, viewport size, resize desynchronisation, and the chart scrolling under the gesture — while the actual cause was visible in one line of the client's own numbers. When the browser is on another machine, instrument it early instead of reproducing locally. ### Overlays are positioned against the plot, not the element **The trendline snap was 66 pixels out, and so was everything else drawn over the chart.** Lightweight Charts reports coordinates from the plot area's origin. The chart *element* also contains the price scales, so once the left scale was enabled for the daily labels, the plot started 66px into the element — and every overlay positioned with `left:` against the element was displaced by exactly that much, in both directions at once: - the cursor's element-x was read as a plot-x, resolving a bar ~66px to the right of the pointer; - the indicator was then drawn at that bar's plot-x interpreted as element-x, landing ~66px left of where the bar is painted. Not near the cursor, not near the bar, and varying with zoom — 66px is a couple of bars at 30m and a dozen at 1m, which is why it looked random rather than offset. Every diagnostic number agreed with itself throughout, because `dot_y`, `expected_y` and `bar_low_y` all derive from the same API and shared the same wrong origin. Self-consistent instrumentation cannot see a systematic error in its own frame of reference. All overlays now live in one container positioned over the plot canvas, so they inherit plot coordinates untranslated: the snap dot and label, the comment layer, the trendline anchor handles, the preview line, the tooltip, the price tag and the context menu. `eventPoint` subtracts the same offset, so a pointer position and a chart coordinate finally mean the same thing. The container is repositioned on resize. This had been mis-diagnosed for hours: device pixel ratio, viewport size, resize desynchronisation, the chart scrolling under the gesture, and the dead band below the candles were each measured and ruled out. The measurement that found it was comparing `canvas.width` to `element.clientWidth` — 0.894 — which is the first thing that ever disagreed with itself.