docs/Plan.md

Now we’re talking like a systems architect 😌🔥

You’re not building “an API”. You’re building a market data engine.

And yes — this is not easy. If it were easy, Bloomberg wouldn’t cost $25,000 per year.

Let’s design this properly.

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🧠 Core Vision

You want:

• Dynamic symbol discovery
• Fast search
• Timeframe awareness
• Date range introspection
• Range-based data extraction
• Scalable architecture
• Low latency
• Production ready

That’s basically a mini Bloomberg / TradingView backend.

Good. Let’s build it in layers.

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🔥 1️⃣ Expanded Real-World Use Cases (Beyond Your 5–10%)

You mentioned symbol list + range query.

That’s only surface-level. Here’s the real ecosystem:

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📊 Market Data Retrieval

1. List all available symbols

2. Search symbols by prefix or fuzzy match

3. Get available timeframes per symbol

4. Get min/max available date for symbol/timeframe

5. Fetch OHLCV data by:

   • symbol
   • timeframe
   • date range

6. Fetch latest N candles

7. Fetch last price snapshot

8. Multi-symbol batch query

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📈 Analytics & Indicators

9. Compute SMA/EMA on the fly
10. RSI/MACD calculation
11. VWAP calculation
12. ATR/Volatility stats
13. Rolling window queries
14. Correlation between symbols
15. Spread analysis
16. Multi-timeframe aggregation

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📦 Metadata Layer

17. Symbol classification (Forex, Crypto, Equity)
18. Exchange mapping
19. Trading session hours
20. Symbol status (active/delisted)
21. Liquidity score

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🚀 Performance / Pro Features

22. Precomputed aggregates
23. Candle compression (1m → 5m)
24. Server-side pagination
25. Async streaming responses
26. Real-time websocket updates
27. Snapshot caching
28. Heatmap endpoint

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🤖 ML & Research Layer

29. Return normalized data
30. Return feature-engineered dataset
31. Backtesting query endpoint
32. Walk-forward validation data splits
33. Dataset slicing for training

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🛡 Operational

34. Rate limiting
35. Access tiers
36. API key auth
37. Usage metrics
38. Request logging
39. Error monitoring

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🏗 2️⃣ Architecture Design (High Performance Version)

Now let’s talk real engineering.

Your biggest enemy:

👉 Time-series query latency 👉 File scanning 👉 Memory overload

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🔥 Recommended Architecture

Layer 1: Storage

Do NOT serve directly from raw HF HTTP every request.

Instead:

Option A (simple):

• Sync parquet files into container on startup

Option B (proper scalable):

• Store in object storage (S3-compatible)
• Use partitioned parquet structure

Example ideal structure:

/data
   /symbol=BTCUSD
       /timeframe=1m
           /year=2024
               part-001.parquet

Partitioning by:

• symbol
• timeframe
• year

This enables very fast filtering.

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Layer 2: Metadata Index (CRITICAL)

Never scan files per request.

Instead build metadata table:

| symbol | timeframe | start_date | end_date | file_path |

Store this in:

• SQLite (simple)
• PostgreSQL (better)
• DuckDB (excellent for time-series)
• Redis (for fast lookups)

This allows instant symbol listing and range discovery.

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Layer 3: Query Engine

When request comes:

1. Validate symbol
2. Validate timeframe
3. Lookup metadata
4. Determine which partitions to read
5. Use PyArrow Dataset filtering
6. Return JSON

PyArrow filtering example:

dataset = ds.dataset("data/", format="parquet")
table = dataset.to_table(
    filter=(
        (ds.field("symbol") == "BTCUSD") &
        (ds.field("timestamp") >= start) &
        (ds.field("timestamp") <= end)
    )
)

This reads only relevant row groups.

That’s how you keep it fast.

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Layer 4: Caching Layer

Add Redis:

• Cache symbol list
• Cache latest candle
• Cache hot queries
• Cache range metadata

Time-based invalidation.

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⚡ 3️⃣ API Design Best Practices

REST Design (Recommended)

GET /symbols
GET /symbols/search?q=btc
GET /symbols/{symbol}/timeframes
GET /symbols/{symbol}/{timeframe}/range
GET /data?symbol=BTCUSD&tf=1m&start=2024-01-01&end=2024-02-01
GET /data/latest?symbol=BTCUSD&tf=1m

Use:

• Query parameters for filters
• Pagination
• Limit max rows

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Response Optimization

• Gzip compression
• Option for CSV or JSON
• Option for binary Arrow format
• Pagination for large ranges

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GraphQL?

Only if you want flexible multi-field selection. For time-series APIs, REST is simpler and faster.

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⚡ 4️⃣ Performance Strategy

Here’s where people fail.

❌ Bad Design

• Load full parquet file
• Filter in pandas
• Return giant JSON

✅ Good Design

• Partitioned storage
• Arrow dataset filtering
• Limit rows
• Cache hot symbols
• Async FastAPI
• Use uvicorn with workers

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⚠ 5️⃣ Major Challenges

1. Large Range Queries

Solution:

• Hard row limits
• Pagination
• Stream responses

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2. Cold Start

Solution:

• Preload metadata
• Warm-up query

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3. Concurrency

Solution:

• Async endpoints
• Gunicorn workers
• Avoid global pandas DataFrames

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4. Memory Pressure

Solution:

• Avoid pandas
• Use PyArrow
• Stream results

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5. Scaling

Solution:

• Stateless API
• External object storage
• Horizontal scaling

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🧠 Advanced Optimization

If you want serious performance:

• Use DuckDB as query engine
• It reads parquet directly
• Very fast filtering
• SQL-based

Example:

import duckdb

con = duckdb.connect()
df = con.execute("""
    SELECT * FROM 'data/*.parquet'
    WHERE symbol='BTCUSD'
    AND timestamp BETWEEN '2024-01-01' AND '2024-02-01'
""").df()

DuckDB is insanely fast for this use case.

Honestly? For your architecture — DuckDB is a better backend engine than pandas.

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🏁 Final Architecture Recommendation

If I were building your system:

Storage → Partitioned Parquet Metadata → SQLite / Postgres Query Engine → DuckDB API → FastAPI (async) Cache → Redis Deployment → Docker Hosting → HF Space (demo) or Cloud VM (production)

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