training-data-pipeline

Personal training data pipeline — pulls activities from the Strava API, computes HR zone distributions, and generates structured Markdown reports suitable for LLM analysis and coaching.

What it does

• Fetches all your Strava activities for a given date range
• Computes per-activity HR zone splits using your personal VT1/VT2 thresholds
• Calculates pace (runs), speed (rides), and VAM (climbing)
• Includes activity descriptions and private notes from Strava
• Outputs clean Markdown reports (weekly, monthly, or single combined)

Example output

# Training log — week 2026-W12 (2026-03-16 – 2026-03-22)

## Week summary

- Total time: 6:45
- Total distance: 58.3 km (32.1 km running, 26.2 km trail run)
- Total elevation: 1,230 m
- Activities: 4 (2 runs, 1 trail run, 1 weight training)
- HR zone distribution (% of tracked time): Z1 12% · Z2 45% · Z3 30% · Z4 13%
- Avg suffer score: 87.5

## Activities

### 2026-03-16 — Easy long run (run)

- Distance: 18.2 km | Moving time: 1:32 | Elevation: +85 m
- Avg pace: 5:04 /km
- HR: avg 142 bpm / max 158 bpm
- Zone split: Z1 25% · Z2 60% · Z3 15%
- Suffer score: 72
- Description: Progressive build to marathon pace last 5k
- Private note: Left calf felt tight after km 12

Setup

1. Create a Strava API application

1. Go to Strava API Settings
2. Create an application (any name/website is fine)
3. Set the Authorization Callback Domain to localhost
4. Note your Client ID and Client Secret

2. Clone and install

git clone <repo-url>
cd training-data-pipeline
pip install -r strava_log/requirements.txt

3. Configure environment

Copy the example env file and fill in your Strava credentials:

cp .env.example .env

Edit .env:

STRAVA_CLIENT_ID=12345
STRAVA_CLIENT_SECRET=abc123def456...

Optional settings

Variable	Default	Description
VT1_BPM	145	Ventilatory threshold 1 (aerobic) heart rate
VT2_BPM	171	Ventilatory threshold 2 (anaerobic) heart rate
MAX_HR	191	Maximum heart rate
OUTPUT_DIR	./training_logs	Directory for generated reports
STRAVA_TOKEN_FILE	.strava_tokens.json	Path to cached OAuth tokens

HR zones are derived from VT1 and VT2:

Zone	Range
Z0	Below 80% of VT1 (recovery)
Z1	80–90% of VT1 (easy aerobic)
Z2	90% of VT1 to VT1 (moderate aerobic)
Z3	VT1 to VT2 (threshold)
Z4	Above VT2 (high intensity)

4. Authorize

On first run, the tool opens a browser-based OAuth flow. It starts a local server on port 8080 to capture the callback:

python -m strava_log.strava_log

Follow the URL printed in the terminal, authorize in your browser, and tokens are saved automatically to .strava_tokens.json.

Usage

# Default: last 4 complete weeks, weekly reports
python -m strava_log.strava_log

# Last 8 weeks
python -m strava_log.strava_log --weeks 8

# Specific date range
python -m strava_log.strava_log --from 2026-01-01 --to 2026-03-25

# Monthly reports instead of weekly
python -m strava_log.strava_log --format monthly

# Single combined report
python -m strava_log.strava_log --format single

# Custom output directory
python -m strava_log.strava_log --output ./reports

# Force re-authentication
python -m strava_log.strava_log --auth

# Quiet mode (no progress output)
python -m strava_log.strava_log --quiet

CLI options

Option	Default	Description
--weeks N	4	Fetch the last N complete weeks
--from DATE	—	Start date (YYYY-MM-DD), overrides --weeks
--to DATE	today	End date (YYYY-MM-DD)
--format	weekly	Report granularity: weekly, monthly, or single
--output DIR	./training_logs	Output directory
--auth	—	Force re-authentication
--quiet	—	Suppress progress output

Project structure

strava_log/
├── strava_log.py   # CLI entry point
├── auth.py         # OAuth2 flow and token management
├── api.py          # Strava API calls (activities, streams, details)
├── config.py       # .env loading and zone boundary calculation
├── process.py      # Activity processing, zone distribution, aggregation
├── render.py       # Markdown report generation
└── requirements.txt