Initial hello world IBKR API connection.

2026-07-25 22:19:31 +03:00
parent 470e243526
commit 8d3e663e71
11 changed files with 456 additions and 3 deletions
@@ -0,0 +1,9 @@
+__pycache__/
+*.py[cod]
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+.venv/
+dist/
+build/
+*.egg-info/
@@ -19,8 +19,10 @@ Keep these areas loosely separated in code and documentation. Avoid coupling the

 - Use Python for implementation.
 - Use `mise` to control the Python version.
+- Use `uv` to manage Python packages and virtual environments.
 - The local Python version is pinned in `.mise.toml`.
 - Prefer commands run through `mise exec -- ...` when the Python environment matters.
+- Prefer `mise exec -- uv run ...` for project Python commands once dependencies are synced.
 - Git is the version control system for this project. A remote will be added later.

 ## Safety And Trading Constraints
@@ -15,15 +15,24 @@ This repository is at the planning and scaffolding stage. The machine learning m

 ## Environment

-Use `mise` to manage Python.
+Use `mise` to manage Python and `uv` to manage Python packages.

 ```sh
 mise install
-mise exec -- python --version
+mise exec -- uv sync
+mise exec -- uv run python --version
 ```

 The current local Python version is pinned in `.mise.toml`.

+## Dependencies
+
+Runtime dependencies are declared in `pyproject.toml`.
+
+- `ib-insync` for the IBKR API connection.
+- `pandas` for tabular candle data handling.
+- `pyarrow` for Parquet file support.
+
 ## Trading Safety

 The default target is paper trading. Real trading should only be added later with explicit configuration, clear documentation, and tests around order generation and broker integration.
@@ -33,3 +42,7 @@ Do not commit secrets such as IBKR credentials, account identifiers, API tokens,
 ## Documentation

 See [docs/README.md](docs/README.md) for the initial architecture notes and decision log.
+
+The first data collection design note is [docs/data-fetcher.md](docs/data-fetcher.md).
+
+Manual test instructions for the IBKR fetcher skeleton are in [docs/manual-test/README.md](docs/manual-test/README.md).
@@ -10,6 +10,10 @@ Build a Python-based trading bot that uses machine learning to determine which a

 Responsible for acquiring and storing market, asset, and any future feature data needed for model training and evaluation.

+The first planned tool is an IBKR daily candle fetcher. It should fetch open, high, low, close, and volume data for a ticker and date range, then eventually persist that data to a ticker-named Parquet file. See [data-fetcher.md](data-fetcher.md).
+
+Parquet files are partitioned by ticker, not by date.
+
 Open decisions:

 - asset universe;
@@ -72,7 +76,7 @@ Possible scope:
 ## Near-Term Priorities

 1. Decide the initial project package structure.
-2. Add Python packaging and dependency management.
+2. Keep Python packaging and dependency management current with `uv`.
 3. Add a minimal configuration system.
 4. Define interfaces for data collection, model artifacts, and broker execution.
 5. Add tests for the core trading decision boundaries before connecting real broker behavior.
@@ -0,0 +1,64 @@
+# Data Fetcher Design
+
+## Initial Goal
+
+Create a Python module/tool that fetches daily candlestick data from the IBKR API for a specific ticker and date range.
+
+The intended example workflow is:
+
+- ticker: `SPY`;
+- date range: `2026-06-01` to `2026-06-30`;
+- bar size: one trading day;
+- fields: open, high, low, close, volume;
+- output: one Parquet file named for the ticker, such as `SPY.parquet`.
+
+## Current Skeleton
+
+The first implementation is intentionally small:
+
+- hard-coded ticker: `SPY`;
+- hard-coded range: `2026-06-01` to `2026-06-05`;
+- hard-coded IBKR Gateway target: `127.0.0.1:4002`;
+- uses the IBKR API through `ib_insync`;
+- prints fetched candles as CSV-like rows;
+- does not write Parquet yet;
+- does not expose CLI arguments yet;
+- does not define storage paths yet.
+
+Run it with:
+
+```sh
+mise exec -- uv run python src/trading_bot/data/fetch_ibkr_daily.py
+```
+
+This expects a local IBKR Gateway session to be running and accepting API connections on `127.0.0.1:4002`.
+
+Manual test instructions are in [manual-test/README.md](manual-test/README.md).
+
+## Intended Future Behavior
+
+Later, this tool should accept a ticker and date range, fetch daily candles from IBKR, normalize the schema, and write the result to a ticker-named Parquet file.
+
+Decided storage behavior:
+
+- Parquet files are partitioned by ticker, not by date.
+- Each ticker should have its own Parquet file, such as `SPY.parquet`.
+
+Candidate output schema:
+
+| Column | Type | Description |
+| --- | --- | --- |
+| `date` | date | Trading session date |
+| `ticker` | string | Asset ticker |
+| `open` | float | Daily open price |
+| `high` | float | Daily high price |
+| `low` | float | Daily low price |
+| `close` | float | Daily close price |
+| `volume` | integer | Daily traded volume |
+
+Open decisions:
+
+- where raw and normalized data files should live;
+- how to handle adjusted versus unadjusted prices;
+- how to handle missing sessions and IBKR pacing limits;
+- whether to use `ib_insync` long term or a lower-level IBKR client wrapper.
@@ -0,0 +1,64 @@
+# Manual Test Instructions
+
+## IBKR Daily Fetcher Skeleton
+
+This test checks the first hard-coded IBKR data fetcher without writing any data files.
+
+The fetcher currently requests:
+
+- gateway: `127.0.0.1:4002`;
+- client id: `101`;
+- ticker: `SPY`;
+- date range: `2026-06-01` to `2026-06-05`;
+- bar size: `1 day`;
+- data type: `TRADES`;
+- regular trading hours only;
+- output: printed CSV-like rows.
+
+## Prerequisites
+
+1. Start IBKR Gateway.
+2. Log in to the paper trading account.
+3. Confirm API access is enabled in IBKR Gateway.
+4. Confirm the API socket port is `4002`.
+5. Confirm no other API client is already using client id `101`.
+6. Sync Python dependencies:
+
+```sh
+mise exec -- uv sync
+```
+
+## Run The Test
+
+From the repository root, run:
+
+```sh
+mise exec -- uv run python src/trading_bot/data/fetch_ibkr_daily.py
+```
+
+## Expected Output
+
+The tool should first print the hard-coded request range and connection target:
+
+```text
+Fetching SPY daily candles from 2026-06-01 to 2026-06-05
+Connecting to IBKR Gateway at 127.0.0.1:4002 with client id 101
+```
+
+If the request succeeds, it should then print a header and one row per returned trading day:
+
+```text
+date,ticker,open,high,low,close,volume
+2026-06-01,SPY,...
+```
+
+Exact prices and volume depend on what IBKR returns.
+
+## Common Issues
+
+- Connection refused: IBKR Gateway is not running, the port is not `4002`, or API access is disabled.
+- Client id already in use: change `IBKR_CLIENT_ID` in the fetcher or disconnect the other client.
+- No historical bars: confirm the account has market data permissions and that IBKR accepts the requested historical data range.
+- Pacing or permission errors: note the IBKR error message before changing the request.
+
+This skeleton does not write Parquet files yet.
@@ -0,0 +1,21 @@
+[project]
+name = "trading-bot"
+version = "0.1.0"
+description = "Python trading bot using machine learning and the IBKR API."
+readme = "README.md"
+requires-python = ">=3.11,<3.12"
+dependencies = [
+    "ib-insync>=0.9.86",
+    "pandas>=2.3.0",
+    "pyarrow>=20.0.0",
+]
+
+[dependency-groups]
+dev = []
+
+[tool.uv]
+package = true
+
+[build-system]
+requires = ["uv_build>=0.8.0,<0.9.0"]
+build-backend = "uv_build"
@@ -0,0 +1 @@
+"""Trading bot package."""
@@ -0,0 +1 @@
+"""Training data collection tools."""
@@ -0,0 +1,120 @@
+"""Skeleton IBKR daily candle fetcher.
+
+This intentionally uses hard-coded values while the project shape is still
+being designed. Parquet persistence and configurable date ranges come later.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import date
+
+
+TICKER = "SPY"
+START_DATE = date(2026, 6, 1)
+END_DATE = date(2026, 6, 5)
+
+IBKR_HOST = "127.0.0.1"
+IBKR_PORT = 4002
+IBKR_CLIENT_ID = 101
+IBKR_CONNECT_TIMEOUT_SECONDS = 10
+
+
+@dataclass(frozen=True)
+class DailyCandle:
+    """Daily OHLCV market data for one trading session."""
+
+    trading_day: date
+    open: float
+    high: float
+    low: float
+    close: float
+    volume: int
+
+
+def fetch_hard_coded_daily_candles() -> list[DailyCandle]:
+    """Fetch one hard-coded week of SPY daily candles from IBKR."""
+
+    try:
+        from ib_insync import IB, Stock  # type: ignore[import-not-found]
+    except ImportError as exc:
+        raise SystemExit(
+            "Missing dependency: ib_insync. Install it before running the "
+            "IBKR fetcher skeleton."
+        ) from exc
+
+    ib = IB()
+    try:
+        print(
+            f"Connecting to IBKR Gateway at {IBKR_HOST}:{IBKR_PORT} "
+            f"with client id {IBKR_CLIENT_ID}"
+        )
+        ib.connect(
+            IBKR_HOST,
+            IBKR_PORT,
+            clientId=IBKR_CLIENT_ID,
+            timeout=IBKR_CONNECT_TIMEOUT_SECONDS,
+        )
+
+        contract = Stock(TICKER, "SMART", "USD")
+        ib.qualifyContracts(contract)
+        bars = ib.reqHistoricalData(
+            contract,
+            endDateTime="20260605 23:59:59 US/Eastern",
+            durationStr="1 W",
+            barSizeSetting="1 day",
+            whatToShow="TRADES",
+            useRTH=True,
+            formatDate=1,
+        )
+
+        if not bars:
+            print("IBKR returned no historical bars for the hard-coded request.")
+
+        candles: list[DailyCandle] = []
+        for bar in bars:
+            trading_day = bar.date if isinstance(bar.date, date) else bar.date.date()
+            if START_DATE <= trading_day <= END_DATE:
+                candles.append(
+                    DailyCandle(
+                        trading_day=trading_day,
+                        open=float(bar.open),
+                        high=float(bar.high),
+                        low=float(bar.low),
+                        close=float(bar.close),
+                        volume=int(bar.volume),
+                    )
+                )
+
+        return candles
+    finally:
+        if ib.isConnected():
+            ib.disconnect()
+
+
+def print_candles(candles: list[DailyCandle]) -> None:
+    """Print candles in a compact table."""
+
+    print("date,ticker,open,high,low,close,volume")
+    for candle in candles:
+        print(
+            f"{candle.trading_day.isoformat()},"
+            f"{TICKER},"
+            f"{candle.open:.2f},"
+            f"{candle.high:.2f},"
+            f"{candle.low:.2f},"
+            f"{candle.close:.2f},"
+            f"{candle.volume}"
+        )
+
+
+def main() -> None:
+    """Run the hard-coded fetcher skeleton."""
+
+    print(f"Fetching {TICKER} daily candles from {START_DATE} to {END_DATE}")
+    candles = fetch_hard_coded_daily_candles()
+    print_candles(candles)
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,154 @@
+version = 1
+revision = 3
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+resolution-markers = [
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+    "sys_platform == 'emscripten'",
+    "sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
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+version = "1.0.3"
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