Data fetcher implementation

Initial hello world IBKR API connection.
2026-07-26 14:18:07 +03:00 · 2026-07-25 22:19:31 +03:00
11 changed files with 671 additions and 3 deletions
@@ -0,0 +1,11 @@
+__pycache__/
+*.py[cod]
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+.venv/
+dist/
+build/
+*.egg-info/
+
+data/ibkr/
@@ -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 +1,82 @@
+# 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 Implementation
+
+The first implementation is intentionally small:
+
+- ticker passed as a required command line argument;
+- end date passed with `--end-date YYYYMMDD`, defaulting to yesterday;
+- end date can also be derived with `--end-date-from-parquet`;
+- duration passed with `--duration`, defaulting to `1 W`;
+- hard-coded IBKR Gateway target: `127.0.0.1:4002`;
+- uses the IBKR API through `ib_insync`;
+- prints fetched candles as CSV-like rows;
+- writes candles to a symbol-named Parquet file;
+- appends to an existing symbol file and keeps one row per date.
+
+Run it with:
+
+```sh
+mise exec -- uv run python src/trading_bot/data/fetch_ibkr_daily.py SPY
+```
+
+To override the requested range:
+
+```sh
+mise exec -- uv run python src/trading_bot/data/fetch_ibkr_daily.py SPY --end-date 20250605 --duration "1 M"
+```
+
+To fetch backward from the oldest date already stored in the symbol file:
+
+```sh
+mise exec -- uv run python src/trading_bot/data/fetch_ibkr_daily.py SPY --end-date-from-parquet
+```
+
+`--end-date` and `--end-date-from-parquet` cannot be used together. If the symbol Parquet file does not exist or has no rows, `--end-date-from-parquet` uses today's US/Eastern date.
+
+This expects a local IBKR Gateway session to be running and accepting API connections on `127.0.0.1:4002`.
+
+By default, output is written to `data/ibkr/daily/SPY.parquet`. Use `--output-dir` to choose another directory.
+
+Manual test instructions are in [manual-test/README.md](manual-test/README.md).
+
+## Intended Future Behavior
+
+Later, this tool should broaden configuration around data source and normalization choices.
+
+Decided storage behavior:
+
+- Parquet files are partitioned by ticker, not by date.
+- Each ticker should have its own Parquet file, such as `SPY.parquet`.
+- The trading date is used as the row key for merges.
+- Re-fetching a date replaces the existing row for that date in that symbol's file.
+
+Candidate output schema:
+
+| Column | Type | Description |
+| --- | --- | --- |
+| `date` | date index | Trading session date |
+| `symbol` | 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:
+
+- 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 +1,86 @@
+# Manual Test Instructions
+
+## IBKR Daily Fetcher
+
+This test checks the IBKR data fetcher and confirms it writes a symbol-named Parquet file.
+
+The fetcher currently requests:
+
+- gateway: `127.0.0.1:4002`;
+- client id: `101`;
+- symbol: provided as a command line argument;
+- end date: provided with `--end-date YYYYMMDD`, defaulting to yesterday;
+- end date can also be derived with `--end-date-from-parquet`;
+- duration: provided with `--duration`, defaulting to `1 W`;
+- bar size: `1 day`;
+- data type: `TRADES`;
+- regular trading hours only;
+- output: printed CSV-like rows and a Parquet file in `data/ibkr/daily/`.
+
+## 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 SPY
+```
+
+To fetch a specific IBKR range, pass an end date and duration:
+
+```sh
+mise exec -- uv run python src/trading_bot/data/fetch_ibkr_daily.py SPY --end-date 20250605 --duration "1 M"
+```
+
+To fetch backward from the oldest date already stored in `data/ibkr/daily/SPY.parquet`:
+
+```sh
+mise exec -- uv run python src/trading_bot/data/fetch_ibkr_daily.py SPY --end-date-from-parquet
+```
+
+`--end-date` and `--end-date-from-parquet` cannot be used together. If the symbol Parquet file does not exist or has no rows, `--end-date-from-parquet` uses today's US/Eastern date.
+
+## Expected Output
+
+The tool should first print the request range and connection target:
+
+```text
+Fetching SPY daily candles ending 2025-06-05 for duration 1 M
+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,symbol,open,high,low,close,volume
+2025-06-05,SPY,...
+```
+
+Exact prices and volume depend on what IBKR returns.
+
+The tool should then write or update:
+
+```text
+data/ibkr/daily/SPY.parquet
+```
+
+If the Parquet file already exists, rows from the latest fetch are merged into it. The trading date is used as the row key, so a symbol file keeps only one row for each date.
+
+## 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.
@@ -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 +1,293 @@
+"""IBKR daily candle fetcher."""
+
+from __future__ import annotations
+
+import argparse
+import re
+from dataclasses import dataclass
+from datetime import date, datetime, timedelta
+from pathlib import Path
+from zoneinfo import ZoneInfo
+
+import pandas as pd
+
+DEFAULT_OUTPUT_DIR = Path("data/ibkr/daily")
+DEFAULT_DURATION = "1 W"
+EASTERN_TZ = ZoneInfo("America/New_York")
+DURATION_PATTERN = re.compile(r"^\d+\s+[SDWMY]$")
+
+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 default_end_date() -> date:
+    """Return yesterday's date in the US/Eastern market timezone."""
+
+    return datetime.now(EASTERN_TZ).date() - timedelta(days=1)
+
+
+def current_market_date() -> date:
+    """Return today's date in the US/Eastern market timezone."""
+
+    return datetime.now(EASTERN_TZ).date()
+
+
+def parse_end_date(value: str) -> date:
+    """Parse an end date in IBKR-friendly YYYYMMDD form."""
+
+    try:
+        return datetime.strptime(value, "%Y%m%d").date()
+    except ValueError as exc:
+        raise argparse.ArgumentTypeError(
+            "end date must use YYYYMMDD format, such as 20250605"
+        ) from exc
+
+
+def parse_duration(value: str) -> str:
+    """Normalize and validate an IBKR duration string like '1 W' or '1 M'."""
+
+    normalized = " ".join(value.upper().split())
+    if not DURATION_PATTERN.fullmatch(normalized):
+        raise argparse.ArgumentTypeError(
+            "duration must look like an IBKR value, such as '1 W' or '1 M'"
+        )
+    return normalized
+
+
+def format_ibkr_end_datetime(end_date: date) -> str:
+    """Convert a date to the US/Eastern end datetime string IBKR expects."""
+
+    return f"{end_date:%Y%m%d} 23:59:59 US/Eastern"
+
+
+def normalize_bar_date(value: date | datetime | str) -> date:
+    """Normalize an IBKR historical bar date value."""
+
+    if isinstance(value, datetime):
+        return value.date()
+    if isinstance(value, date):
+        return value
+    return parse_end_date(value)
+
+
+def fetch_daily_candles(
+    symbol: str, end_date: date, duration: str
+) -> list[DailyCandle]:
+    """Fetch daily candles for a symbol 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."
+        ) 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(symbol, "SMART", "USD")
+        ib.qualifyContracts(contract)
+        bars = ib.reqHistoricalData(
+            contract,
+            endDateTime=format_ibkr_end_datetime(end_date),
+            durationStr=duration,
+            barSizeSetting="1 day",
+            whatToShow="TRADES",
+            useRTH=True,
+            formatDate=1,
+        )
+
+        if not bars:
+            print(f"IBKR returned no historical bars for {symbol}.")
+
+        candles: list[DailyCandle] = []
+        for bar in bars:
+            trading_day = normalize_bar_date(bar.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 candles_to_frame(symbol: str, candles: list[DailyCandle]) -> pd.DataFrame:
+    """Convert candles to a date-indexed dataframe ready for Parquet storage."""
+
+    rows = [
+        {
+            "date": candle.trading_day,
+            "symbol": symbol,
+            "open": candle.open,
+            "high": candle.high,
+            "low": candle.low,
+            "close": candle.close,
+            "volume": candle.volume,
+        }
+        for candle in candles
+    ]
+    frame = pd.DataFrame.from_records(rows)
+    if frame.empty:
+        return pd.DataFrame(
+            columns=["symbol", "open", "high", "low", "close", "volume"],
+            index=pd.Index([], name="date"),
+        )
+
+    frame["date"] = pd.to_datetime(frame["date"]).dt.date
+    return frame.set_index("date")
+
+
+def read_existing_candles(path: Path) -> pd.DataFrame:
+    """Read an existing candle Parquet file as a date-indexed dataframe."""
+
+    if not path.exists():
+        return pd.DataFrame(
+            columns=["symbol", "open", "high", "low", "close", "volume"],
+            index=pd.Index([], name="date"),
+        )
+
+    frame = pd.read_parquet(path)
+    if "date" in frame.columns:
+        frame["date"] = pd.to_datetime(frame["date"]).dt.date
+        frame = frame.set_index("date")
+
+    frame.index = pd.to_datetime(frame.index).date
+    frame.index.name = "date"
+    return frame
+
+
+def oldest_stored_date_or_today(path: Path) -> date:
+    """Return the oldest stored candle date, or today if no data exists yet."""
+
+    existing = read_existing_candles(path)
+    if existing.empty:
+        return current_market_date()
+    return min(existing.index)
+
+
+def write_candles(path: Path, symbol: str, candles: list[DailyCandle]) -> pd.DataFrame:
+    """Append candles to a ticker Parquet file, keeping one row per date."""
+
+    existing = read_existing_candles(path)
+    fetched = candles_to_frame(symbol, candles)
+    combined = pd.concat([existing, fetched])
+    if not combined.empty:
+        combined = combined[~combined.index.duplicated(keep="last")]
+        combined = combined.sort_index()
+
+    path.parent.mkdir(parents=True, exist_ok=True)
+    combined.to_parquet(path, index=True)
+    return combined
+
+
+def print_candles(symbol: str, candles: list[DailyCandle]) -> None:
+    """Print candles in a compact table."""
+
+    print("date,symbol,open,high,low,close,volume")
+    for candle in candles:
+        print(
+            f"{candle.trading_day.isoformat()},"
+            f"{symbol},"
+            f"{candle.open:.2f},"
+            f"{candle.high:.2f},"
+            f"{candle.low:.2f},"
+            f"{candle.close:.2f},"
+            f"{candle.volume}"
+        )
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse command line arguments."""
+
+    parser = argparse.ArgumentParser(description="Fetch daily IBKR candles.")
+    parser.add_argument("symbol", help="Ticker symbol to fetch, such as SPY.")
+    end_date_group = parser.add_mutually_exclusive_group()
+    end_date_group.add_argument(
+        "--end-date",
+        type=parse_end_date,
+        help="Request end date in YYYYMMDD format. Defaults to yesterday.",
+    )
+    end_date_group.add_argument(
+        "--end-date-from-parquet",
+        action="store_true",
+        help=(
+            "Use the oldest date from the symbol Parquet file as the request "
+            "end date. Defaults to today if the file is missing or empty."
+        ),
+    )
+    parser.add_argument(
+        "--duration",
+        type=parse_duration,
+        default=DEFAULT_DURATION,
+        help=(
+            "IBKR duration string, such as '1 W' or '1 M'. "
+            f"Defaults to {DEFAULT_DURATION}."
+        ),
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=DEFAULT_OUTPUT_DIR,
+        help=f"Directory for Parquet files. Defaults to {DEFAULT_OUTPUT_DIR}.",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    """Run the daily candle fetcher."""
+
+    args = parse_args()
+    symbol = args.symbol.upper()
+    output_path = args.output_dir / f"{symbol}.parquet"
+    end_date = (
+        oldest_stored_date_or_today(output_path)
+        if args.end_date_from_parquet
+        else args.end_date or default_end_date()
+    )
+
+    print(
+        f"Fetching {symbol} daily candles ending {end_date:%Y-%m-%d} "
+        f"for duration {args.duration}"
+    )
+    candles = fetch_daily_candles(symbol, end_date, args.duration)
+    print_candles(symbol, candles)
+    stored = write_candles(output_path, symbol, candles)
+    print(f"Wrote {len(stored)} total daily rows to {output_path}")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,154 @@
+version = 1
+revision = 3
+requires-python = "==3.11.*"
+resolution-markers = [
+    "sys_platform == 'win32'",
+    "sys_platform == 'emscripten'",
+    "sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "eventkit"
+version = "1.0.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/16/1e/0fac4e45d71ace143a2673ec642701c3cd16f833a0e77a57fa6a40472696/eventkit-1.0.3.tar.gz", hash = "sha256:99497f6f3c638a50ff7616f2f8cd887b18bbff3765dc1bd8681554db1467c933", size = 28320, upload-time = "2023-12-11T11:41:35.339Z" }
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+]
+
+[[package]]
+name = "ib-insync"
+version = "0.9.86"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "eventkit" },
+    { name = "nest-asyncio" },
+]
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+version = "1.6.0"
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Author	SHA1	Message	Date
jarno	e2eb5dd574	Data fetcher implementation	2026-07-26 14:18:07 +03:00
jarno	8d3e663e71	Initial hello world IBKR API connection.	2026-07-25 22:19:31 +03:00