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jarno c1c02e2b0d Documentation update 2026-08-11 20:23:59 +03:00
jarno 900b70d6df Removed IBKR related code 2026-08-11 20:13:41 +03:00
12 changed files with 205 additions and 720 deletions
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AGENTS.md
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@@ -2,7 +2,7 @@
## Project Purpose
This repository is for a Python trading bot that uses machine learning to decide which assets should be held. The bot will use the IBKR API to facilitate trades, starting with paper trading and allowing a carefully controlled path to real trading later.
This repository is for a Python trading bot that uses machine learning to decide which assets should be held. The bot uses the Alpaca Markets API for market data and paper trading, with any future live trading behind a carefully controlled path.
## Current Direction
README.md
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@@ -1,10 +1,10 @@
# Trading Bot
Python trading bot project using machine learning to decide which assets should be held. The bot is intended to trade through the IBKR API, beginning with paper trading and potentially supporting real trades later behind explicit safeguards.
Python trading bot project using machine learning to decide which assets should be held. The bot currently uses the Alpaca Markets API for market data and paper trading, with any future live trading kept behind explicit safeguards.
## Project Status
This repository is at the planning and scaffolding stage. The machine learning model, trading parameters, asset universe, and risk rules will be designed later.
This repository now has a working prototype flow for Alpaca market data, dataset generation, XGBoost training, prediction, and paper-trading rebalancing. The strategy is still intentionally narrow: it currently focuses on `SPY` exposure using a small market-regime feature set.
## Main Parts
@@ -29,22 +29,53 @@ The current local Python version is pinned in `.mise.toml`.
Runtime dependencies are declared in `pyproject.toml`.
- `ib-insync` for the IBKR API connection.
- `alpaca-py` for Alpaca market data and trading clients.
- `pandas` for tabular candle data handling.
- `pyarrow` for Parquet file support.
- `xgboost` and `scikit-learn` for model training and evaluation.
- `python-dotenv` for loading local Alpaca credentials from `.env`.
## 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.
The default target is Alpaca paper trading. Real trading should only be added later with explicit configuration, clear documentation, and tests around order generation and broker integration.
Do not commit secrets such as IBKR credentials, account identifiers, API tokens, or private configuration.
Do not commit secrets such as Alpaca API keys, account identifiers, API tokens, or private configuration.
## Current Commands
Fetch Alpaca daily candles:
```sh
mise exec -- uv run python src/trading_bot/data/fetch_alpaca_daily.py SPY
```
Build the training dataset and train the current model:
```sh
mise exec -- uv run python src/trading_bot/data/train_pipeline.py
```
Run the current Alpaca paper-trading rebalance flow:
```sh
mise exec -- uv run python src/trading_bot/models/trade.py \
--paper \
--fetch-recent-data \
--model-path models/spy_xgb_v1.json \
--metadata-path models/spy_xgb_v1_meta.json
```
The main Python entry points are:
- `src/trading_bot/data/train_pipeline.py` for dataset generation and model training.
- `src/trading_bot/models/trade.py` for Alpaca account inspection and SPY rebalancing.
## 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), and the first training dataset contract is [docs/training-dataset.md](docs/training-dataset.md).
The current data collection note is [docs/data-fetcher.md](docs/data-fetcher.md), and the training dataset contract is [docs/training-dataset.md](docs/training-dataset.md).
The first dataset generation notebook is [notebooks/spy_direction_dataset.ipynb](notebooks/spy_direction_dataset.ipynb).
Manual test instructions for the IBKR fetcher skeleton are in [docs/manual-test/README.md](docs/manual-test/README.md).
Manual test instructions for the Alpaca fetcher are in [docs/manual-test/README.md](docs/manual-test/README.md).
docs/README.md
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@@ -2,7 +2,7 @@
## Objective
Build a Python-based trading bot that uses machine learning to determine which assets should be held, then uses the IBKR API to facilitate trades. The first supported trading mode should be paper trading.
Build a Python-based trading bot that uses machine learning to determine which assets should be held, then uses Alpaca Markets for market data and broker execution. The first supported trading mode is paper trading.
## Planned Modules
@@ -10,9 +10,9 @@ 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).
The current data tool is an Alpaca daily candle fetcher. It fetches open, high, low, close, and volume data for a ticker and date range, then persists that data to a ticker-named Parquet file. See [data-fetcher.md](data-fetcher.md).
The initial supervised training dataset is documented in [training-dataset.md](training-dataset.md). It derives market-regime features from `SPY`, `VIX`, `TLT`, and `USO`, then labels whether `SPY` closes higher five trading days later.
The initial supervised training dataset is documented in [training-dataset.md](training-dataset.md). It derives market-regime features from `SPY`, a volatility proxy, `TLT`, and `USO`, then labels whether `SPY` closes higher five trading days later. The checked-in configuration currently maps the volatility input to `VIXY` for Alpaca data availability.
Parquet files are partitioned by ticker, not by date.
@@ -29,6 +29,8 @@ Open decisions:
Responsible for building datasets, training models, evaluating candidates, and writing versioned model artifacts.
The main entry point is `src/trading_bot/data/train_pipeline.py`. It reads raw Alpaca Parquet files from `data/alpaca/daily`, builds `data/training/spy_direction_5d.parquet`, trains an XGBoost classifier, and writes model artifacts to `models/`.
Open decisions:
- prediction target;
@@ -41,11 +43,13 @@ Open decisions:
### Trading Bot
Responsible for loading a model, generating portfolio signals, deciding target holdings, and using the IBKR API to place or simulate orders.
Responsible for loading a model, generating portfolio signals, deciding target holdings, and using the Alpaca trading API to place paper-trading orders.
The main entry point is `src/trading_bot/models/trade.py`. It loads a model and metadata, optionally refreshes recent Alpaca market data, estimates a target `SPY` exposure from the model probability, cancels open Alpaca orders, and submits a day market order when the desired rebalance exceeds the configured minimum notional amount. Pass `--model-path models/spy_xgb_v1.json --metadata-path models/spy_xgb_v1_meta.json` to trade with artifacts produced by the current training pipeline.
Initial expectations:
- paper trading first;
- Alpaca paper trading first;
- real trading later only behind explicit configuration;
- clear logging of model version, signals, target holdings, generated orders, and broker responses;
- separation between signal generation, portfolio construction, and broker execution.
@@ -56,7 +60,7 @@ Open decisions:
- position sizing;
- risk limits;
- cash handling;
- order types;
- order types and time-in-force choices;
- failed order handling;
- market hours behavior;
- manual override behavior.
@@ -79,9 +83,9 @@ Possible scope:
1. Decide the initial project package structure.
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.
4. Harden interfaces for data collection, model artifacts, and Alpaca broker execution.
5. Expand tests around trading decision boundaries, portfolio sizing, stale data handling, and broker API boundaries.
## Decisions Deferred
The model, features, labels, asset selection rules, risk management rules, and trading cadence are intentionally deferred for later discussion.
Broader model design, asset selection rules, risk management rules, live-trading gates, and trading cadence are intentionally deferred for later discussion.
docs/data-fetcher.md
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## Initial Goal
Create a Python module/tool that fetches daily candlestick data from the IBKR API for a specific ticker and date range.
Create a Python module/tool that fetches daily candlestick data from the Alpaca Market Data API for a specific ticker and date range.
The intended example workflow is:
@@ -14,41 +14,40 @@ The intended example workflow is:
## Current Implementation
The first implementation is intentionally small:
The current 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;
- uses the Alpaca Market Data API through `alpaca-py`;
- reads credentials from `--api-key` / `--secret-key`, `ALPACA_API_KEY` / `ALPACA_SECRET_KEY`, or `.env`;
- 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
mise exec -- uv run python src/trading_bot/data/fetch_alpaca_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"
mise exec -- uv run python src/trading_bot/data/fetch_alpaca_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
mise exec -- uv run python src/trading_bot/data/fetch_alpaca_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`.
This expects Alpaca API credentials to be available via CLI arguments, environment variables, or `.env`.
By default, output is written to `data/ibkr/daily/SPY.parquet`. Use `--output-dir` to choose another directory.
By default, output is written to `data/alpaca/daily/SPY.parquet`. Use `--output-dir` to choose another directory.
Manual test instructions are in [manual-test/README.md](manual-test/README.md).
@@ -78,5 +77,5 @@ Candidate output schema:
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.
- how to handle missing sessions, market holidays, and Alpaca API rate limits;
- whether to add explicit feed selection, adjustment settings, or data entitlement checks.
docs/manual-test/README.md
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# Manual Test Instructions
## IBKR Daily Fetcher
## Alpaca Daily Fetcher
This test checks the IBKR data fetcher and confirms it writes a symbol-named Parquet file.
This test checks the Alpaca 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/`.
- data source: Alpaca stock bars through `alpaca-py`;
- output: a Parquet file in `data/alpaca/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:
1. Create or confirm Alpaca API credentials.
2. Provide credentials through `ALPACA_API_KEY` and `ALPACA_SECRET_KEY`, a local `.env`, or CLI arguments.
3. Sync Python dependencies:
```sh
mise exec -- uv sync
@@ -35,52 +29,51 @@ mise exec -- uv sync
From the repository root, run:
```sh
mise exec -- uv run python src/trading_bot/data/fetch_ibkr_daily.py SPY
mise exec -- uv run python src/trading_bot/data/fetch_alpaca_daily.py SPY
```
To fetch a specific IBKR range, pass an end date and duration:
To fetch a specific Alpaca 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"
mise exec -- uv run python src/trading_bot/data/fetch_alpaca_daily.py SPY --end-date 20250605 --duration "1 M"
```
To fetch backward from the oldest date already stored in `data/ibkr/daily/SPY.parquet`:
To fetch backward from the oldest date already stored in `data/alpaca/daily/SPY.parquet`:
```sh
mise exec -- uv run python src/trading_bot/data/fetch_ibkr_daily.py SPY --end-date-from-parquet
mise exec -- uv run python src/trading_bot/data/fetch_alpaca_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:
The tool should first print the request range:
```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
Fetching SPY daily candles ending 2025-06-05 for duration 1 M via Alpaca API
```
If the request succeeds, it should then print a header and one row per returned trading day:
If the request succeeds, it should print how many candles were fetched and where the merged Parquet file was written:
```text
date,symbol,open,high,low,close,volume
2025-06-05,SPY,...
Fetched 21 daily candles for SPY.
Wrote 21 total daily rows to data/alpaca/daily/SPY.parquet
```
Exact prices and volume depend on what IBKR returns.
Exact row counts depend on the requested date range and market calendar.
The tool should then write or update:
```text
data/ibkr/daily/SPY.parquet
data/alpaca/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.
- Missing credentials: set `ALPACA_API_KEY` and `ALPACA_SECRET_KEY` or pass them with CLI flags.
- No historical bars: confirm the symbol, requested range, and Alpaca market data permissions.
- Authentication or entitlement errors: confirm the keys belong to the intended Alpaca account and data plan.
- Rate-limit errors: wait before retrying or reduce repeated requests.
docs/training-dataset.md
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## Initial Goal
Build a supervised learning dataset from stored IBKR daily candle data. The first dataset predicts whether `SPY` closes higher five trading days after the observation date.
Build a supervised learning dataset from stored Alpaca daily candle data. The first dataset predicts whether `SPY` closes higher five trading days after the observation date.
The first implementation is [../notebooks/spy_direction_dataset.ipynb](../notebooks/spy_direction_dataset.ipynb), a Python notebook using pandas. This keeps the feature calculations inspectable while the dataset design is still changing. Once the feature contract settles, reusable loading and feature-building code can move into `src/trading_bot/models` or `src/trading_bot/data`.
The current implementation is `src/trading_bot/data/train_pipeline.py`, which combines the original dataset notebook and XGBoost training notebook into one runnable script. The earlier notebook, [../notebooks/spy_direction_dataset.ipynb](../notebooks/spy_direction_dataset.ipynb), remains useful background for inspecting the initial feature design.
## Raw Data
Raw daily candles are stored under:
```text
data/ibkr/daily
data/alpaca/daily
```
Storage expectations:
@@ -21,10 +21,10 @@ Storage expectations:
- each row contains one daily candle for that symbol;
- expected columns are `date`, `symbol`, `open`, `high`, `low`, `close`, and `volume`.
The initial dataset requires at least these symbols:
The initial dataset requires at least these logical inputs:
- `SPY`;
- `VIX`;
- volatility proxy, currently `VIXY` in `config/train_config.json`;
- `TLT`;
- `USO`.
@@ -57,7 +57,7 @@ The target column is a binary indicator of `SPY` forward return over the next fi
| --- | --- |
| `spy_up_5d` | `1.0` when `SPY` closes above today's close five trading days later; `0.0` when `SPY` closes below today's close five trading days later; `0.5` when the future close equals today's close. |
Using `0.5` for unchanged prices preserves the row while making the target explicitly neutral.
The original dataset design used `0.5` for unchanged prices as an explicit neutral target. The current training pipeline drops unchanged `0.5` rows before training so the XGBoost model remains a binary classifier.
## Data Alignment
@@ -109,9 +109,24 @@ Training, validation, and test splits should be chronological:
A split indicator column such as `split` is useful in the dataset artifact for auditability and reproducibility. It should be treated as metadata, not as a model input feature. The model training code should build `X` from the explicit feature column list and exclude metadata columns such as `date`, `split`, raw close prices, and the target.
## Training Pipeline
Run the current pipeline from the repository root:
```sh
mise exec -- uv run python src/trading_bot/data/train_pipeline.py
```
The script reads configuration from `config/train_config.json`, writes the dataset to `data/training/spy_direction_5d.parquet`, and saves model artifacts to:
- `models/spy_xgb_v1.json`;
- `models/spy_xgb_v1_meta.json`.
The metadata file stores the training base probability, feature column list, last trained date, and model configuration.
## Open Decisions
- exact adjusted versus unadjusted close handling;
- whether same-day `VIX`, `TLT`, and `USO` values are acceptable for the intended trading decision timing;
- whether same-day volatility proxy, `TLT`, and `USO` values are acceptable for the intended trading decision timing;
- exact train, validation, and test date boundaries or split percentages;
- output dataset file location, schema metadata, and versioning.
- output dataset schema metadata and versioning.
notebooks/ibkr_scratch.ipynb
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@@ -1,228 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "cd849b8e",
"metadata": {},
"source": [
"# IBKR scratch notebook\n",
"\n",
"This notebook is for quick manual testing of the IBKR gateway connection, portfolio lookup, and a simple SPY order flow.\n",
"\n",
"> Use this only with a paper-trading or test setup unless you explicitly intend to submit a live order.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "543426a2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connecting to IBKR Gateway at 127.0.0.1:4002 with client id 101...\n",
"Failed to connect to IBKR Gateway: This event loop is already running\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Error 200, reqId 9: No security definition has been found for the request, contract: Stock(symbol='VUAA', exchange='SMART', currency='USD')\n",
"Error 200, reqId 10: No security definition has been found for the request\n",
"Canceled order: Trade(contract=Stock(symbol='VUAA', exchange='SMART', currency='USD'), order=MarketOrder(orderId=10, clientId=101, action='BUY', totalQuantity=0.7), orderStatus=OrderStatus(orderId=10, status='Cancelled', filled=0.0, remaining=0.0, avgFillPrice=0.0, permId=0, parentId=0, lastFillPrice=0.0, clientId=0, whyHeld='', mktCapPrice=0.0), fills=[], log=[TradeLogEntry(time=datetime.datetime(2026, 7, 30, 19, 36, 57, 539380, tzinfo=datetime.timezone.utc), status='PendingSubmit', message='', errorCode=0), TradeLogEntry(time=datetime.datetime(2026, 7, 30, 19, 36, 57, 743208, tzinfo=datetime.timezone.utc), status='Cancelled', message='Error 200, reqId 10: No security definition has been found for the request', errorCode=200)], advancedError='')\n",
"Peer closed connection.\n"
]
}
],
"source": [
"import sys\n",
"from pathlib import Path\n",
"\n",
"from ib_insync import IB, Stock\n",
"\n",
"# Allow the notebook to import local source code from the repository.\n",
"repo_root = Path.cwd().resolve()\n",
"if (repo_root / \"src\").exists():\n",
" repo_root = repo_root\n",
"else:\n",
" repo_root = repo_root.parent\n",
"\n",
"src_path = repo_root / \"src\"\n",
"if str(src_path) not in sys.path:\n",
" sys.path.insert(0, str(src_path))\n",
"\n",
"IBKR_HOST = \"127.0.0.1\"\n",
"IBKR_PORT = 4002\n",
"IBKR_CLIENT_ID = 101\n",
"IBKR_CONNECT_TIMEOUT_SECONDS = 10\n",
"\n",
"ib = IB()\n",
"print(f\"Connecting to IBKR Gateway at {IBKR_HOST}:{IBKR_PORT} with client id {IBKR_CLIENT_ID}...\")\n",
"try:\n",
" ib.connect(IBKR_HOST, IBKR_PORT, clientId=IBKR_CLIENT_ID, timeout=IBKR_CONNECT_TIMEOUT_SECONDS)\n",
" print(\"Connection established.\")\n",
"except Exception as e:\n",
" print(f\"Failed to connect to IBKR Gateway: {e}\")\n",
" ib.disconnect()\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ecdc721f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connected: True\n",
"Client ID: 101\n"
]
}
],
"source": [
"print(f\"Connected: {ib.isConnected()}\")\n",
"print(f\"Client ID: {ib.client.clientId}\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "10ebc240",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No open portfolio positions were returned.\n"
]
}
],
"source": [
"portfolio = ib.portfolio()\n",
"if not portfolio:\n",
" print(\"No open portfolio positions were returned.\")\n",
"else:\n",
" for item in portfolio:\n",
" print(\n",
" f\"{item.contract.symbol}: position={item.position}, \"\n",
" f\"market_value={item.marketValue}, unrealized_pnl={item.unrealizedPNL}\"\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "fed654ec",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Unknown contract: Stock(symbol='VUAA', exchange='SMART', currency='USD')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Submitted order for VUAA: Trade(contract=Stock(symbol='VUAA', exchange='SMART', currency='USD'), order=MarketOrder(orderId=10, clientId=101, action='BUY', totalQuantity=0.7), orderStatus=OrderStatus(orderId=10, status='PendingSubmit', filled=0.0, remaining=0.0, avgFillPrice=0.0, permId=0, parentId=0, lastFillPrice=0.0, clientId=0, whyHeld='', mktCapPrice=0.0), fills=[], log=[TradeLogEntry(time=datetime.datetime(2026, 7, 30, 19, 36, 57, 539380, tzinfo=datetime.timezone.utc), status='PendingSubmit', message='', errorCode=0)], advancedError='')\n"
]
}
],
"source": [
"from ib_insync import MarketOrder\n",
"\n",
"contract = Stock(\"VUAA\", \"SMART\", \"USD\")\n",
"await ib.qualifyContractsAsync(contract)\n",
"\n",
"# Adjust the quantity as needed before running this cell.\n",
"order = MarketOrder(\"BUY\", 0.70)\n",
"trade = ib.placeOrder(contract, order)\n",
"\n",
"print(f\"Submitted order for {contract.symbol}: {trade}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "06881101",
"metadata": {},
"outputs": [],
"source": [
"trds = ib.trades()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "08663602",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Cancelled', 'Cancelled', 'Cancelled']"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(map(lambda x: x.orderStatus.status, trds))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "2c7abf23",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Trade(contract=Stock(conId=756733, symbol='SPY', right='?', exchange='SMART', currency='USD', localSymbol='SPY', tradingClass='SPY'), order=Order(permId=1578393268, action='BUY', totalQuantity=1.0, orderType='MKT', lmtPrice=0.0, auxPrice=0.0, tif='DAY', ocaType=3, displaySize=2147483647, rule80A='0', openClose='', volatilityType=0, deltaNeutralOrderType='None', referencePriceType=0, account='DUR281921', clearingIntent='IB', cashQty=0.0, dontUseAutoPriceForHedge=True, filledQuantity=0.0, refFuturesConId=2147483647, shareholder='Not an insider or substantial shareholder'), orderStatus=OrderStatus(orderId=0, status='Cancelled', filled=0.0, remaining=0.0, avgFillPrice=0.0, permId=0, parentId=0, lastFillPrice=0.0, clientId=0, whyHeld='', mktCapPrice=0.0), fills=[], log=[], advancedError=''),\n",
" Trade(contract=Stock(conId=756733, symbol='SPY', right='?', exchange='SMART', currency='USD', localSymbol='SPY', tradingClass='SPY'), order=Order(permId=24474350, action='BUY', totalQuantity=0.1345, orderType='MKT', lmtPrice=0.0, auxPrice=0.0, tif='DAY', ocaType=3, displaySize=2147483647, rule80A='0', openClose='', volatilityType=0, deltaNeutralOrderType='None', referencePriceType=0, account='DUR281921', clearingIntent='IB', cashQty=0.0, dontUseAutoPriceForHedge=True, filledQuantity=0.0, refFuturesConId=2147483647, shareholder='Not an insider or substantial shareholder'), orderStatus=OrderStatus(orderId=0, status='Cancelled', filled=0.0, remaining=0.0, avgFillPrice=0.0, permId=0, parentId=0, lastFillPrice=0.0, clientId=0, whyHeld='', mktCapPrice=0.0), fills=[], log=[], advancedError=''),\n",
" Trade(contract=Stock(symbol='VUAA', exchange='SMART', currency='USD'), order=MarketOrder(orderId=10, clientId=101, action='BUY', totalQuantity=0.7), orderStatus=OrderStatus(orderId=10, status='Cancelled', filled=0.0, remaining=0.0, avgFillPrice=0.0, permId=0, parentId=0, lastFillPrice=0.0, clientId=0, whyHeld='', mktCapPrice=0.0), fills=[], log=[TradeLogEntry(time=datetime.datetime(2026, 7, 30, 19, 36, 57, 539380, tzinfo=datetime.timezone.utc), status='PendingSubmit', message='', errorCode=0), TradeLogEntry(time=datetime.datetime(2026, 7, 30, 19, 36, 57, 743208, tzinfo=datetime.timezone.utc), status='Cancelled', message='Error 200, reqId 10: No security definition has been found for the request', errorCode=200)], advancedError='')]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trds"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
notebooks/spy_direction_dataset.ipynb
+93 -93
View File
@@ -87,16 +87,16 @@
"output_type": "stream",
"text": [
"<class 'pandas.DataFrame'>\n",
"DatetimeIndex: 1255 entries, 2021-08-02 to 2026-07-31\n",
"DatetimeIndex: 1258 entries, 2021-08-02 to 2026-08-05\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 SPY_close 1255 non-null float64\n",
" 1 VIX_close 1255 non-null float64\n",
" 2 TLT_close 1255 non-null float64\n",
" 3 USO_close 1255 non-null float64\n",
" 0 SPY_close 1258 non-null float64\n",
" 1 VIX_close 1258 non-null float64\n",
" 2 TLT_close 1258 non-null float64\n",
" 3 USO_close 1258 non-null float64\n",
"dtypes: float64(4)\n",
"memory usage: 49.0 KB\n"
"memory usage: 49.1 KB\n"
]
},
{
@@ -234,7 +234,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -364,7 +364,7 @@
"2021-10-15 -0.002202 0.030287 -0.003823 1.0 "
]
},
"execution_count": 4,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -418,7 +418,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -458,24 +458,24 @@
" <tbody>\n",
" <tr>\n",
" <th>train</th>\n",
" <td>840</td>\n",
" <td>842</td>\n",
" <td>2021-10-11</td>\n",
" <td>2025-02-13</td>\n",
" <td>0.583333</td>\n",
" <td>2025-02-18</td>\n",
" <td>0.581948</td>\n",
" </tr>\n",
" <tr>\n",
" <th>validation</th>\n",
" <td>180</td>\n",
" <td>2025-02-14</td>\n",
" <td>2025-10-31</td>\n",
" <td>0.633333</td>\n",
" <td>2025-02-19</td>\n",
" <td>2025-11-04</td>\n",
" <td>0.638889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>test</th>\n",
" <td>181</td>\n",
" <td>2025-11-03</td>\n",
" <td>2026-07-24</td>\n",
" <td>0.569061</td>\n",
" <td>182</td>\n",
" <td>2025-11-05</td>\n",
" <td>2026-07-29</td>\n",
" <td>0.576923</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -484,12 +484,12 @@
"text/plain": [
" rows start_date end_date target_mean\n",
"split \n",
"train 840 2021-10-11 2025-02-13 0.583333\n",
"validation 180 2025-02-14 2025-10-31 0.633333\n",
"test 181 2025-11-03 2026-07-24 0.569061"
"train 842 2021-10-11 2025-02-18 0.581948\n",
"validation 180 2025-02-19 2025-11-04 0.638889\n",
"test 182 2025-11-05 2026-07-29 0.576923"
]
},
"execution_count": 5,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -543,17 +543,17 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rows: 1,201\n",
"Rows: 1,204\n",
"Feature columns: ['SPY_ret_5', 'SPY_ret_20', 'SPY_dist_sma50', 'VIX_change_5', 'VIX_rank_20', 'TLT_ret_10', 'USO_ret_5', 'SPY_TLT_ratio_ret']\n",
"Target column: spy_up_5d\n",
"Training matrix shape: (840, 8)\n"
"Training matrix shape: (842, 8)\n"
]
},
{
@@ -592,16 +592,16 @@
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1201.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1201</td>\n",
" <td>1204.000000</td>\n",
" <td>1204.000000</td>\n",
" <td>1204.000000</td>\n",
" <td>1204.000000</td>\n",
" <td>1204.000000</td>\n",
" <td>1204.000000</td>\n",
" <td>1204.000000</td>\n",
" <td>1204.000000</td>\n",
" <td>1204.000000</td>\n",
" <td>1204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
@@ -640,32 +640,32 @@
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>840</td>\n",
" <td>842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.002502</td>\n",
" <td>0.009848</td>\n",
" <td>0.011028</td>\n",
" <td>0.017763</td>\n",
" <td>0.403476</td>\n",
" <td>-0.004099</td>\n",
" <td>0.005024</td>\n",
" <td>0.004880</td>\n",
" <td>0.588676</td>\n",
" <td>0.002464</td>\n",
" <td>0.009816</td>\n",
" <td>0.010973</td>\n",
" <td>0.017820</td>\n",
" <td>0.404506</td>\n",
" <td>-0.004103</td>\n",
" <td>0.004938</td>\n",
" <td>0.004838</td>\n",
" <td>0.589701</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.023057</td>\n",
" <td>0.043558</td>\n",
" <td>0.037315</td>\n",
" <td>0.299417</td>\n",
" <td>0.337501</td>\n",
" <td>0.028502</td>\n",
" <td>0.052628</td>\n",
" <td>0.027606</td>\n",
" <td>0.492279</td>\n",
" <td>0.023044</td>\n",
" <td>0.043515</td>\n",
" <td>0.037287</td>\n",
" <td>0.299050</td>\n",
" <td>0.337773</td>\n",
" <td>0.028469</td>\n",
" <td>0.052606</td>\n",
" <td>0.027586</td>\n",
" <td>0.492092</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
@@ -683,40 +683,40 @@
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.009601</td>\n",
" <td>-0.016509</td>\n",
" <td>-0.008656</td>\n",
" <td>-0.056615</td>\n",
" <td>-0.009749</td>\n",
" <td>-0.016557</td>\n",
" <td>-0.008704</td>\n",
" <td>-0.056606</td>\n",
" <td>0.100000</td>\n",
" <td>-0.023304</td>\n",
" <td>-0.025866</td>\n",
" <td>-0.010013</td>\n",
" <td>-0.023139</td>\n",
" <td>-0.025950</td>\n",
" <td>-0.010029</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.003798</td>\n",
" <td>0.015725</td>\n",
" <td>0.017802</td>\n",
" <td>-0.018570</td>\n",
" <td>0.003749</td>\n",
" <td>0.015639</td>\n",
" <td>0.017755</td>\n",
" <td>-0.018430</td>\n",
" <td>0.300000</td>\n",
" <td>-0.004435</td>\n",
" <td>0.004950</td>\n",
" <td>0.005537</td>\n",
" <td>-0.004429</td>\n",
" <td>0.004869</td>\n",
" <td>0.005508</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.015955</td>\n",
" <td>0.038179</td>\n",
" <td>0.038282</td>\n",
" <td>0.030814</td>\n",
" <td>0.015950</td>\n",
" <td>0.038167</td>\n",
" <td>0.038124</td>\n",
" <td>0.030815</td>\n",
" <td>0.750000</td>\n",
" <td>0.014227</td>\n",
" <td>0.032486</td>\n",
" <td>0.021390</td>\n",
" <td>0.014188</td>\n",
" <td>0.032295</td>\n",
" <td>0.021358</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
@@ -739,33 +739,33 @@
],
"text/plain": [
" SPY_ret_5 SPY_ret_20 SPY_dist_sma50 VIX_change_5 VIX_rank_20 \\\n",
"count 1201.000000 1201.000000 1201.000000 1201.000000 1201.000000 \n",
"count 1204.000000 1204.000000 1204.000000 1204.000000 1204.000000 \n",
"unique NaN NaN NaN NaN NaN \n",
"top NaN NaN NaN NaN NaN \n",
"freq NaN NaN NaN NaN NaN \n",
"mean 0.002502 0.009848 0.011028 0.017763 0.403476 \n",
"std 0.023057 0.043558 0.037315 0.299417 0.337501 \n",
"mean 0.002464 0.009816 0.010973 0.017820 0.404506 \n",
"std 0.023044 0.043515 0.037287 0.299050 0.337773 \n",
"min -0.114962 -0.123975 -0.141995 -0.387709 0.050000 \n",
"25% -0.009601 -0.016509 -0.008656 -0.056615 0.100000 \n",
"50% 0.003798 0.015725 0.017802 -0.018570 0.300000 \n",
"75% 0.015955 0.038179 0.038282 0.030814 0.750000 \n",
"25% -0.009749 -0.016557 -0.008704 -0.056606 0.100000 \n",
"50% 0.003749 0.015639 0.017755 -0.018430 0.300000 \n",
"75% 0.015950 0.038167 0.038124 0.030815 0.750000 \n",
"max 0.082843 0.157566 0.088105 3.846154 1.000000 \n",
"\n",
" TLT_ret_10 USO_ret_5 SPY_TLT_ratio_ret spy_up_5d split \n",
"count 1201.000000 1201.000000 1201.000000 1201.000000 1201 \n",
"count 1204.000000 1204.000000 1204.000000 1204.000000 1204 \n",
"unique NaN NaN NaN NaN 3 \n",
"top NaN NaN NaN NaN train \n",
"freq NaN NaN NaN NaN 840 \n",
"mean -0.004099 0.005024 0.004880 0.588676 NaN \n",
"std 0.028502 0.052628 0.027606 0.492279 NaN \n",
"freq NaN NaN NaN NaN 842 \n",
"mean -0.004103 0.004938 0.004838 0.589701 NaN \n",
"std 0.028469 0.052606 0.027586 0.492092 NaN \n",
"min -0.092880 -0.196652 -0.117208 0.000000 NaN \n",
"25% -0.023304 -0.025866 -0.010013 0.000000 NaN \n",
"50% -0.004435 0.004950 0.005537 1.000000 NaN \n",
"75% 0.014227 0.032486 0.021390 1.000000 NaN \n",
"25% -0.023139 -0.025950 -0.010029 0.000000 NaN \n",
"50% -0.004429 0.004869 0.005508 1.000000 NaN \n",
"75% 0.014188 0.032295 0.021358 1.000000 NaN \n",
"max 0.091322 0.327273 0.129204 1.000000 NaN "
]
},
"execution_count": 6,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -787,14 +787,14 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(PosixPath('/home/jarno/repos/trading-bot/data/training/spy_direction_5d.parquet'),\n",
" (1201, 11),\n",
" (1204, 11),\n",
" date SPY_ret_5 SPY_ret_20 SPY_dist_sma50 VIX_change_5 \\\n",
" 0 2021-10-11 0.014114 -0.026625 -0.018145 -0.090869 \n",
" 1 2021-10-12 0.001201 -0.023752 -0.020386 -0.074091 \n",
@@ -810,7 +810,7 @@
" 4 0.05 -0.002202 0.030287 -0.003823 1.0 train )"
]
},
"execution_count": 7,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
pyproject.toml
+1 -2
View File
@@ -1,12 +1,11 @@
[project]
name = "trading-bot"
version = "0.1.0"
description = "Python trading bot using machine learning and the IBKR API."
description = "Python trading bot using machine learning and the Alpaca Markets API."
readme = "README.md"
requires-python = ">=3.11,<3.12"
dependencies = [
"alpaca-py>=0.43.5",
"ib-insync>=0.9.86",
"matplotlib>=3.11.1",
"pandas>=2.3.0",
"pyarrow>=20.0.0",
src/trading_bot/data/fetch_alpaca_daily.py
+1
View File
@@ -98,6 +98,7 @@ def main() -> None:
api_key=args.api_key,
secret_key=args.secret_key,
)
print(f"Fetched {len(candles)} daily candles for {symbol}.")
stored = write_candles(output_path, symbol, candles)
print(f"Wrote {len(stored)} total daily rows to {output_path}")
src/trading_bot/data/fetch_ibkr_daily.py
-293
View File
@@ -1,293 +0,0 @@
"""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()
uv.lock
Generated
-36
View File
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{ name = "nest-asyncio" },
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