TapeDeck

An MCP server engineered like production infrastructure: context-budgeted responses, an adversarially-tested SQL sandbox, measured query benchmarks, and zero hallucinated numbers.

TapeDeck gives any Model Context Protocol client a compact market-data and quant-research surface over a local Parquet lake. It combines DuckDB SQL analytics, causal indicators, an event-driven next-open backtester, and a hostile-input SQL guard. It never routes an order and needs no API key.

The committed offline dataset contains 203,460 labelled sample rows: 120,000 synthetic Poisson/regime-switching aggregate trades for BTCUSDT and ETHUSDT, plus 83,460 synthetic volatility-clustered daily bars for 20 equities from 2010-01-04 through 2025-12-31. Every tool returns source_label, so an agent cannot quietly present sample observations as live market facts.

flowchart LR
  A["Binance archive"] --> I["Idempotent ingest"]
  B["Stooq daily CSV"] --> I
  C["Deterministic offline samples"] --> I
  I --> P["Hive-partitioned Parquet"]
  P --> D["DuckDB views and macros"]
  D --> G["SQL AST guard"]
  D --> Q["Indicators, scans, microstructure"]
  D --> E["Next-open event backtester"]
  G --> M["Budgeted FastMCP tools"]
  Q --> M
  E --> M
  M --> Z["Codex, Claude, and MCP clients"]

60-second offline demo

Python 3.11 or newer is required. After the one-time dependency installation, every demo, test, benchmark, and server operation is local; only the two explicitly named ingest scripts can make network requests.

python3.11 -m venv .venv
.venv/bin/pip install -e '.[dev]'
make demo

The demo connects in memory through a real FastMCP client, calls all nine tools, reads all three resources, renders all three prompts, and writes the complete result to data/demo_output.json. Re-running make samples is idempotent.

The verified build uses the external fastmcp package (version 2.14.7 in the recorded environment), rather than the fallback FastMCP bundled with the official MCP Python SDK.

Tool surface

Tool	What it returns
list_symbols	Asset class, coverage, row count, sources, and live/sample label
get_bars	Equity daily bars or crypto 1s/1m/5m/1h/1d OHLCV, VWAP, and count
compute_indicators	Causal SMA, EMA, RSI, returns, realized vol, z-score, and drawdown
scan_market	Ranked momentum, z-score, volatility, and 52-week-break snapshots
volume_profile	Price-bucketed volume and point of control
order_flow	Crypto aggressor-side buy/sell volume and normalized imbalance
run_backtest	CAGR, Sharpe plus bootstrap CI, drawdown, turnover, trades, and curve
sql_query	One AST-validated, read-only, timed and row-capped DuckDB SELECT
describe_schema	Agent-readable view, macro, column, and semantic documentation

Detailed signatures and real request/response fragments are in docs/TOOLS.md. The tapedeck://manifest, tapedeck://schema, and tapedeck://strategies resources support agent planning; analyze_symbol, daily_market_review, and strategy_evaluation provide evidence-disciplined prompt flows.

Real demo fragment

This is trimmed from data/demo_output.json, produced on the committed sample lake:

{
  "scan_market": {
    "rank": 1,
    "symbol": "MSFT",
    "zscore_20d": 2.108462,
    "momentum_252d": 0.16218052,
    "source_label": "sample"
  },
  "sma_crossover_SPY": {
    "cagr": 0.03190133,
    "sharpe": 0.27361,
    "sharpe_ci_95": [-0.21129888, 0.75640762],
    "max_drawdown": -0.43555141,
    "trade_count": 59
  }
}

These values describe deterministic synthetic data. They are software evidence, not market evidence or an investment result.

Measured benchmark headlines

<!-- BENCHMARK_HEADLINES_START -->

• Resampled 120,000 ticks to one-minute bars in 7.423 ms warm (19.275 ms fresh-connection median).
• Ran the 16-year SPY SMA backtest in 91.130 ms warm (146.534 ms including data reload).
• Wrote Parquet at 2,038,130 rows/second over 5 measured runs. <!-- BENCHMARK_HEADLINES_END -->

Definitions, machine details, 20-run medians, and raw JSON are in docs/BENCHMARKS.md and data/bench.json. make bench rewrites the two artifacts and synchronizes the block above, preventing hand-edited performance claims.

Run and connect

make serve                 # stdio
make serve-http PORT=8765  # streamable HTTP at http://127.0.0.1:8765/mcp

Ready-to-paste files and commands for Codex, Claude Code, Claude Desktop, generic stdio, streamable HTTP, and MCP Inspector are in clients/README.md. The Codex instructions use the current shared host configuration:

codex mcp add tapedeck -- /Users/sanjeevkumar/Documents/TapeDeck/.venv/bin/python -m tapedeck.server

Security model

sql_query parses with sqlglot before DuckDB sees text. It admits one SELECT-family AST, rejects mutation/configuration/extensions/external readers, confines permitted Parquet/CSV paths beneath data/, wraps a row limit, and interrupts a timed query. Execution uses a read-only DuckDB connection with a 256 MB session memory limit. The test suite contains 30 explicit injection and escape attempts plus row-cap and hard-interruption cases. See docs/SECURITY.md for the threat model and residual risks.

Data ingestion

The only network-capable commands are explicit:

.venv/bin/python scripts/ingest_binance.py --symbols BTCUSDT ETHUSDT --days 2026-07-29
.venv/bin/python scripts/ingest_stooq.py --symbols SPY AAPL MSFT

Both skip existing partitions, use finite timeouts, write atomically, refresh data/manifest.json, and materialize the committed deterministic samples after any fetch failure. make ingest-offline exercises that fallback without opening a network connection. Tick data is partitioned by symbol and UTC day. Daily data uses symbol/year partitions to avoid tens of thousands of one-row Parquet files while retaining a session-level date column.

Limitations

• Equities are end-of-day, split-unadjusted OHLCV; equity volume profiles allocate each session's volume to its close, and equity order flow is unavailable.
• The committed lake is deliberately synthetic and labelled sample; run the ingest scripts to add source data where network access permits.
• The deployment is single-node and unauthenticated. Bind HTTP to localhost unless an authenticating reverse proxy and tenant isolation are added.
• Aggregate trades are not a limit-order book and cannot reconstruct queue position or L2 depth.
• Backtests are research simulations with a simplified cost model, not executable returns.

Verification

make test
make lint
make demo
make bench
make ingest-offline

Design rationale is in docs/DESIGN.md, and worked agent flows are in docs/COOKBOOK.md.

Research and education only. TapeDeck does not place trades and is not financial advice.