chart/docs/plan.md

# /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.

> **Companion document:** `docs/implementation.md` records what actually
> happened — the problems hit while building this and how each was resolved.
> This file is the plan and the reasoning; that one is the experience. When they
> disagree, the log is what really occurred.


---

## 0. Start here

> **This document is now mostly history.** M0–M10 are built and deployed. The
> build order, branch instructions and "existing repo state" that used to open
> this file described a greenfield app and were actively misleading by August
> 2026, so they are gone. What remains below is the reasoning behind decisions
> already made — read it to understand *why* something works the way it does,
> not to find out what to build.
>
> **For current work, start with `AGENTS.md`**, which every agent loads
> automatically. It points at the live planning documents:
> `docs/NEXT_STEPS.md` for the short list, `docs/async_refactor.md`,
> `docs/multi_user.md`, `docs/feature_undo.md` and `docs/mobile_enhance.md` for
> designs not yet built.
>
> **`main` deploys to production.** A push triggers a Forgejo webhook and
> Coolify rebuild of <https://chart.amow.com>. That is the intended workflow now,
> not an accident to avoid — but it means every push is a deploy, and a deploy
> restarts the market stream.
>
> §16 onward is a dated log of problems and their resolutions. It is the most
> useful part of this file for anyone debugging: most entries record something
> that looked like one bug and was another.

### 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
<script src="https://unpkg.com/vue@3/dist/vue.global.prod.js"></script>
<script src="https://unpkg.com/lightweight-charts@5.2.0/dist/lightweight-charts.standalone.production.js"></script>
```

### 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`; warned in `Procfile` |
| JWT signing key derived from the password | A leaked cookie brute-forces the password offline; blocks multi-user outright | Server-side random secret — `docs/multi_user.md` |
| Threadpool routes touching `asyncio.Queue` | Dropped socket wakeups, rare corruption | Post via `call_soon_threadsafe` — `docs/async_refactor.md` P0 |
| Level rebuilds run CPU-bound on the event loop | 82s startup; a stall every closed bar | Bulk seed, incremental MAs — `docs/async_refactor.md` P1 |
| Alert disarm writes to disk on the loop | Stream stalls when an alert fires | Offload the write — `docs/async_refactor.md` P2 |
| 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 |