Add spec 2 for later implementation

.claude/specs/spec-02.md

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+# Task 02: Signals, CSV Signal Loader, and CLI
+
+## Goal
+
+Add the signal system and a basic CLI so a customer can bring their own
+forecast data (as CSV), reference it from their algo, and run a backtest
+from the command line rather than writing a Python script.
+
+This builds on task-01. All existing types, the DA matching engine, and
+the PnL/VWAP analysis remain unchanged.
+
+---
+
+## What to build
+
+### 1. `signals/base.py`
+
+The `SignalProvider` protocol and supporting types:
+
+```python
+class SignalSchema:
+    """Describes what a signal provides."""
+    name: str
+    dtype: type          # float, int, str
+    frequency: timedelta # How often it updates
+    description: str
+    unit: str            # "EUR/MWh", "MW", "m/s", "celsius", etc.
+
+class SignalValue:
+    """A single signal observation."""
+    timestamp: datetime  # When this value is valid for
+    value: Any           # The actual value
+
+class SignalProvider(Protocol):
+    @property
+    def name(self) -> str: ...
+
+    @property
+    def schema(self) -> SignalSchema: ...
+
+    def get_value(self, timestamp: datetime) -> SignalValue: ...
+    def get_range(self, start: datetime, end: datetime) -> pd.Series: ...
+```
+
+Use Pydantic v2 for `SignalSchema` and `SignalValue`.
+
+### 2. `signals/registry.py`
+
+A `SignalRegistry` that holds registered signal providers and provides
+lookup by name. Nothing fancy - a dict with validation that the signal
+exists when the algo asks for it.
+
+```python
+class SignalRegistry:
+    def register(self, provider: SignalProvider) -> None: ...
+    def get(self, name: str) -> SignalProvider: ...
+    def has(self, name: str) -> bool: ...
+    def list_signals(self) -> list[str]: ...
+```
+
+### 3. `signals/csv_loader.py`
+
+A `CsvSignalProvider` that loads a CSV file and serves it as a signal.
+This is the simplest way for a customer to bring their own data without
+implementing `SignalProvider` from scratch.
+
+Expected CSV format:
+```csv
+timestamp,value
+2026-03-15T00:00:00+01:00,42.31
+2026-03-15T00:15:00+01:00,41.87
+2026-03-15T00:30:00+01:00,43.05
+```
+
+Columns: `timestamp` (required, parsed as timezone-aware datetime) and
+`value` (required, parsed as float). Additional columns are ignored.
+
+The provider must support an optional `publication_offset` parameter to
+prevent look-ahead bias. The offset represents how far ahead of the
+delivery period the forecast is published. A value with timestamp T
+(the period it describes) is visible to the algo at time
+T - publication_offset (when it was published).
+
+For example, a wind forecast published 6 hours ahead would use
+`publication_offset=timedelta(hours=6)`. The forecast for the 06:00
+delivery period was published at 00:00, so it becomes visible when the
+simulated clock reaches 00:00. In code: `get_value(current_time)` returns
+the latest value where `timestamp <= current_time + publication_offset`.
+
+If `publication_offset` is not set, values are available at their timestamp
+(i.e., the value for 06:00 is available at 06:00). This is correct for
+actuals/historical data but would be look-ahead bias for forecasts. Log
+a warning when no offset is set, suggesting the user consider whether
+their data represents forecasts or actuals.
+
+```python
+signal = CsvSignalProvider(
+    name="my_wind_forecast",
+    path="data/wind_forecast_NO1.csv",
+    unit="MW",
+    description="Wind generation forecast for NO1",
+    publication_offset=timedelta(hours=6),
+)
+```
+
+### 4. Update `context.py`
+
+Add the signal methods to `TradingContext` (they exist as stubs from
+task-01, now they need real signatures):
+
+```python
+def get_signal(self, name: str) -> SignalValue: ...
+def get_signal_history(self, name: str, lookback: int) -> list[SignalValue]: ...
+```
+
+### 5. Update `engines/backtest.py`
+
+Wire signals into the `BacktestEngine`:
+
+- Accept a `signals` parameter (list of `SignalProvider` instances)
+- Register them in a `SignalRegistry`
+- When the algo calls `ctx.get_signal(name)`, look up the provider and
+  return the value for the current simulated time, respecting
+  `publication_offset`
+- When the algo calls `ctx.get_signal_history(name, lookback=N)`, return
+  the last N values up to and including the current time
+
+The backtest context implementation needs to enforce the look-ahead bias
+rule: at simulated time T, `get_signal` must not return any value whose
+publication time is after T.
+
+### 6. Update `algo.py`
+
+Add `subscribe_signal(name)` to `SimpleAlgo` and the `on_signal` hook:
+
+```python
+class SimpleAlgo:
+    def subscribe_signal(self, name: str) -> None:
+        """Register interest in a signal. Called during on_setup."""
+
+    def on_signal(self, ctx: TradingContext, name: str, value: SignalValue) -> None:
+        """Called when a subscribed signal updates. Override to react."""
+```
+
+For DA backtesting, `on_signal` is called at the start of each auction
+period with the latest signal values. The algo can also pull signals
+directly via `ctx.get_signal()` from any hook.
+
+### 7. `cli/main.py`
+
+A minimal CLI using `click`:
+
+```bash
+# Run a backtest
+nexa run examples/simple_da_algo.py \
+    --exchange nordpool \
+    --start 2026-03-01 \
+    --end 2026-03-31 \
+    --products NO1_DA \
+    --data-dir ./data \
+    --capital 100000
+
+# Output: the BacktestResult.summary() text
+```
+
+The CLI needs to:
+- Accept a path to a Python file containing an algo
+- Import the file and find the algo (look for a subclass of `SimpleAlgo`
+  or a function decorated with `@algo`)
+- Instantiate the `BacktestEngine` with the provided arguments
+- Call `.run()` and print `result.summary()`
+
+Use `importlib` to dynamically load the algo module. If the module contains
+exactly one `SimpleAlgo` subclass, use it. If it contains multiple, raise
+an error asking the user to specify which one.
+
+Register the CLI entry point in `pyproject.toml`:
+
+```toml
+[project.scripts]
+nexa = "nexa_backtest.cli.main:cli"
+```
+
+### 8. Update the example algo
+
+Update `examples/simple_da_algo.py` to use a signal:
+
+```python
+class ForecastAlgo(SimpleAlgo):
+    """Buy when DA price is below a provided price forecast."""
+
+    def on_setup(self, ctx: TradingContext) -> None:
+        self.subscribe_signal("price_forecast")
+        self.threshold = 5.0
+
+    def on_auction_open(self, ctx: TradingContext, auction: AuctionInfo) -> None:
+        forecast = ctx.get_signal("price_forecast").value
+        if forecast is not None:
+            ctx.place_order(Order.buy(
+                product=auction.product_id,
+                volume_mw=10,
+                price_eur=Decimal(str(forecast)) - Decimal(str(self.threshold)),
+            ))
+```
+
+Create a corresponding `examples/data/price_forecast_NO1.csv` with
+synthetic forecast values (slightly noisy version of the actual clearing
+prices from the test fixture, offset by the publication delay).
+
+The example should be runnable via:
+```bash
+nexa run examples/simple_da_algo.py \
+    --exchange nordpool \
+    --start 2026-03-01 \
+    --end 2026-03-31 \
+    --products NO1_DA \
+    --data-dir tests/fixtures \
+    --capital 100000
+```
+
+---
+
+## How signals are passed to the CLI
+
+For this task, signal CSV files are discovered by convention. The engine
+looks in `{data_dir}/signals/` for CSV files matching the signal name:
+
+```
+data_dir/
+  signals/
+    price_forecast.csv
+    wind_forecast.csv
+```
+
+If the algo subscribes to a signal called "price_forecast", the engine
+looks for `{data_dir}/signals/price_forecast.csv`. If the file doesn't
+exist, raise a `DataError` with a clear message.
+
+This is deliberately simple. A more flexible signal configuration (YAML
+config file, CLI flags per signal, explicit paths) is a later concern.
+
+---
+
+## Tests
+
+1. **signals/base.py**: SignalSchema and SignalValue construction
+2. **signals/csv_loader.py**:
+   - Load a valid CSV, retrieve values at known timestamps
+   - publication_offset prevents future values from being visible
+   - Missing file raises DataError
+   - Malformed CSV (missing columns, bad timestamps) raises DataError
+3. **signals/registry.py**: register, get, has, get missing raises error
+4. **backtest.py integration**: algo that uses a signal to make trading
+   decisions. Verify that the signal value influences the fills (e.g.,
+   algo only buys when forecast is below threshold, verify it doesn't
+   buy when forecast is above threshold)
+5. **cli/main.py**: test that the CLI loads an algo module, finds the
+   SimpleAlgo subclass, and runs without error (use click's CliRunner)
+6. **look-ahead bias**: test that a signal with publication_offset does
+   NOT return a value before its publication time
+
+---
+
+## What NOT to build
+
+- Built-in signal providers (DayAheadPriceSignal, WindForecastSignal, etc.)
+- Signal providers that fetch from APIs
+- YAML/JSON signal configuration
+- `nexa validate` CLI command
+- `nexa compile` CLI command
+- `nexa report` CLI command
+- Any IDC or windowed replay changes
+- HTML report generation
+
+---
+
+## Acceptance criteria
+
+1. `make ci` passes
+2. A customer can load a CSV file as a signal and use it in their algo
+3. publication_offset correctly prevents look-ahead bias
+4. `nexa run` CLI works end-to-end with the example algo
+5. The example algo uses a signal to make trading decisions and produces
+   a PnL summary
+6. All new types have type hints, frozen Pydantic models where appropriate
+7. All new public API has Google-style docstrings

.vscode/settings.json

@@ -0,0 +1,29 @@
+{
+    "cSpell.words": [
+        "Backtest",
+        "Backtrader",
+        "bidkit",
+        "cython",
+        "docstrings",
+        "EPEX",
+        "intraday",
+        "marketdata",
+        "matplotlib",
+        "Mypy",
+        "nexa",
+        "Nord",
+        "nordpool",
+        "nuitka",
+        "numpy",
+        "ONNX",
+        "plotly",
+        "pyarrow",
+        "pydantic",
+        "pytest",
+        "quants",
+        "Scikit",
+        "sklearn",
+        "VWAP",
+        "Zipline"
+    ]
+}