tradestation-downloader 1.0.2

Download and analyze historical futures data from TradeStation API

TradeStation Historical Data Downloader

Automated download of 1-minute futures data from TradeStation API with incremental updates, rate limiting, and Parquet storage.

Features

• OAuth2 Authentication - Automatic token refresh
• Incremental Updates - Only downloads new bars since last run
• Rate Limiting - Respects API limits with configurable delays
• Parquet Storage - Fast, compressed columnar format
• Resume Capability - Interrupted downloads can be resumed
• All US Futures - Pre-configured list of ~50 continuous contracts

Quick Start

1. Install

# From PyPI (recommended)
pip install tradestation-downloader

# Or from source using uv
pip install uv
uv sync

# Or standard pip from source
pip install -e .

# For development (includes pytest, ruff)
pip install -e ".[dev]"

2. Get TradeStation API Credentials

1. Go to TradeStation Developer Portal
2. Create an application
3. Get your client_id, client_secret, and refresh_token

3. Configure

Option A - Interactive setup (recommended):

tradestation-auth

Option B - Manual setup:

cp config.yaml.template config.yaml
# Edit config.yaml with your credentials

4. Run

After install, CLI commands are available:

# Download all configured symbols (incremental)
tradestation-download

# Download specific symbols only
tradestation-download -s "@ES" "@NQ" "@CL"

# Full download (ignore existing data)
tradestation-download --full

# Use daily or monthly partitioned storage
tradestation-download --storage-format daily
tradestation-download --storage-format monthly

# Use different compression (default: zstd)
tradestation-download --compression snappy

# List all default symbols
tradestation-download --list-symbols

# List symbol categories
tradestation-download --list-categories

# Download specific category
tradestation-download --category index

# Save without datetime index (raw format)
tradestation-download -s @ES --no-datetime-index

Note: On Windows, the @ symbol has special meaning in CMD/PowerShell. Always quote symbols: "@ES" instead of @ES.

Or run directly with Python:

python tradestation_downloader.py
python tradestation_downloader.py -s "@ES" "@NQ" "@CL"

Configuration

Edit config.yaml:

tradestation:
  client_id: "YOUR_CLIENT_ID"
  client_secret: "YOUR_CLIENT_SECRET"
  refresh_token: "YOUR_REFRESH_TOKEN"

data_dir: "./data"
start_date: "2007-01-01"
interval: 1
unit: "Minute"
storage_format: "single"  # single, daily, or monthly
compression: "zstd"       # zstd, snappy, gzip, lz4, or none

symbols:
  - "@ES"    # E-mini S&P 500
  - "@NQ"    # E-mini Nasdaq 100
  # ... add more symbols

Output Format

Data is saved as Parquet files in the data_dir. Structure depends on storage format:

Single file (with datetime index):

data/
├── @ES_index_1_1min.parquet
├── @NQ_index_1_1min.parquet
└── @CL_index_1_1min.parquet

Monthly partitioned (default with datetime index):

data/
├── @ES_index_1/
│   ├── year_month=2024-01/data-0.parquet
│   ├── year_month=2024-02/data-0.parquet
│   └── ...
└── @NQ_index_1/
    └── ...

Each file contains:

Column	Type	Description
datetime	datetime	Bar timestamp (UTC)
open	float	Opening price
high	float	High price
low	float	Low price
close	float	Closing price
volume	int	Total volume

DateTime Index Mode

By default, data is saved with datetime as the DataFrame index, enabling pandas time-series operations like resample() and time-based slicing. This adds an _index_1 suffix to folder names.

Default (recommended):

tradestation-download -s @ES

→ Creates @ES_index_1/ folder with datetime as index

Raw format (datetime as column):

tradestation-download -s @ES --no-datetime-index

→ Creates @ES/ folder with datetime as regular column

Why use datetime index?

• df.resample('5min') works directly
• Time slicing: df['2024-01':'2024-06']
• Compatible with pandas time-series functions
• Matches common conventions for OHLCV data

Folder structure with index (default):

data/
├── @ES_index_1/
│   ├── year_month=2024-01/data-0.parquet
│   └── year_month=2024-02/data-0.parquet
└── @NQ_index_1/
    └── ...

Loading Data

import pandas as pd

# Load single symbol (monthly partitioned with datetime index)
df = pd.read_parquet("data/@ES_index_1")
print(df.head())

# Resample to 5-minute bars (works because datetime is the index)
df_5min = df.resample('5min').agg({
    'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last', 'volume': 'sum'
}).dropna()

# Time-based slicing
df_2024 = df['2024-01':'2024-12']

# Load single file format
df = pd.read_parquet("data/ES_1min.parquet")

Python API

pip install tradestation-downloader

Use programmatically in your project:

from tradestation import TradeStationDownloader, DownloadConfig, StorageFormat, Compression

config = DownloadConfig(
    client_id="your_client_id",
    client_secret="your_client_secret",
    refresh_token="your_refresh_token",
    symbols=["@ES"],
    data_dir="./data",
    start_date="2020-01-01",
    storage_format=StorageFormat.SINGLE,
    compression=Compression.ZSTD,  # or SNAPPY, GZIP, LZ4, NONE
)

downloader = TradeStationDownloader(config)
data = downloader.download_all()

# Access download statistics
stats = downloader.stats
print(f"Downloaded {stats.bars_downloaded} bars")
print(f"Processed {stats.symbols_processed} symbols")
print(f"Errors: {stats.errors}")

# Use the data
es_df = data["@ES"]
print(es_df.head())

Or load from config file:

from tradestation import TradeStationDownloader, load_config

config = load_config("config.yaml")
config.symbols = ["@ES", "@NQ"]  # Override symbols

downloader = TradeStationDownloader(config)
downloader.download_all()

Scheduling Daily Updates

Linux/Mac (cron)

# Edit crontab
crontab -e

# Add line to run daily at 6 AM
0 6 * * * cd /path/to/tradestation_downloader && python tradestation_downloader.py >> logs/download.log 2>&1

Windows (Task Scheduler)

1. Open Task Scheduler
2. Create Basic Task
3. Set trigger: Daily at 6:00 AM
4. Action: Start a program
5. Program: python
6. Arguments: C:\path\to\tradestation_downloader.py
7. Start in: C:\path\to\tradestation_downloader

Data Validation

import pandas as pd
from pathlib import Path

data_dir = Path("./data")

for f in data_dir.glob("*_1min.parquet"):
    df = pd.read_parquet(f)
    symbol = f.stem.replace("_1min", "")
    
    print(f"{symbol}:")
    print(f"  Date range: {df['datetime'].min()} to {df['datetime'].max()}")
    print(f"  Total bars: {len(df):,}")
    print(f"  Missing dates: {df['datetime'].diff().gt(pd.Timedelta(minutes=2)).sum()}")
    print()

Troubleshooting

"401 Unauthorized" Error

Your refresh token may have expired. Run tradestation-auth to get a new one.

"429 Rate Limited" Error

The script handles this automatically, but if persistent:

• Increase rate_limit_delay in config
• Reduce number of symbols per run

Missing Data

Some symbols may not have data going back to 2007. Check TradeStation's data availability for specific contracts.

Large Download Size

1-minute data from 2007 is ~500MB-1GB per symbol. Total for all US futures: ~30-50GB.

Default Symbols

Run tradestation-download --list-symbols to see all default symbols:

• Equity Index: @ES, @NQ, @YM, @RTY, @MES, @MNQ, etc.
• Energy: @CL, @NG, @RB, @HO, @BRN
• Metals: @GC, @SI, @HG, @PL, @PA
• Treasury: @US, @TY, @FV, @TU, @UB, @TEN, @TWE
• Grains: @C, @S, @W, @KW, @BO, @SM
• Softs: @KC, @SB, @CT, @CC, @OJ, @LBR
• Meats: @LC, @LH, @FC
• Currency: @EC, @JY, @BP, @AD, @CD, @SF, @DX
• Volatility: @VX
• Crypto: @BTC, @ETH, @MBT, @MET

License

MIT License - Free to use and modify.

Support

For TradeStation API issues: TradeStation Developer Support