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Getting started

From nothing to a real research result with Claude driving it, in six steps. Each one works on its own, and you can stop at any of them.

You need a terminal. Steps 1–2 need nothing else — no keys, no account, no network.


1. Install it

uv tool install tradeflow-engine

No uv? Install it first, or use pipx install tradeflow-engine. This gives you a tradeflow command.

tradeflow --version

That prints the version, which copy is running, and — worth noting now — where its state lives. An installed copy keeps its research journal, trial history, and cache in ~/.tradeflow.

2. See it work, with no keys at all

tradeflow demo

This runs the entire pipeline on synthetic data: it backtests every bundled strategy, picks the one that looks best, walk-forward validates it, and refuses to promote it.

The refusal is the point. The synthetic series is a seeded random walk with no edge in it, and a strategy that looks profitable in-sample gets called noise out-of-sample. If that had not happened, the tool would be broken. Watching it work on data you know is worthless is the fastest way to understand what it is for.

Nothing so far touched the network or needed an account.


3. Get keys and add them

Real market data needs free Alpaca paper-trading credentials:

  1. Sign up at alpaca.markets.
  2. Go to Paper Account → API Keys and generate a key and secret.

You want the paper keys. Paper and live credentials are different, and this tool defaults to paper for a reason.

tradeflow init

The wizard prompts for both (hidden — nothing lands in your shell history), checks them against Alpaca with one cheap request, confirms paper trading is on, and offers to warm a small local data cache. It writes ~/.tradeflow/.env.

Check it any time:

tradeflow init --check

That reports every setting independently — what is set, what is missing, whether optional extras are installed — and writes nothing.

4. Your first real result

tradeflow verdict --symbols NVDA,AAPL,META,AMD,TSLA --start 2024-01-01 --end 2024-12-31

One command runs the whole cross-sectional pipeline — scan, alphas, portfolio construction, information analysis — over one universe, one window, and one cost model, and ends in a single verdict.

Read the verdict line first, then the checks under it. You will most likely get mixed or not promotable, and that is the normal outcome. Every check shows its value and its threshold, so you can see exactly which part failed:

CheckWhat it means when it fails
ic_tstatThe signal's accuracy is not distinguishable from luck
ir_above_noiseThe realized information ratio is inside its own error band — indistinguishable from zero
sanity_ceilingA result too good on public data — suspect a bug or a data leak, not skill
sample_sizeToo few rebalances to measure anything with confidence
net_of_cost_alphaThere may be an edge, but trading costs eat it

Want it as a file you can keep or share?

tradeflow verdict --symbols NVDA,AAPL,META --start 2024-01-01 --end 2024-12-31 --html result.html

That writes one self-contained page — charts embedded, no external requests when opened.

5. Connect Claude

uv tool install --force "tradeflow-engine[mcp]"

Then register the server with your MCP client.

Claude Code:

claude mcp add tradeflow -- tradeflow mcp

Claude Desktop — add to claude_desktop_config.json:

{
"mcpServers": {
"tradeflow": { "command": "tradeflow", "args": ["mcp"] }
}
}

Restart the client and ask it something:

What has this campaign already tried, and what was the best result?

Run a verdict on NVDA, AAPL and META for 2024 and tell me honestly whether there's an edge.

Explain what the deflated Sharpe ratio is correcting for.

Claude cannot trade. The server constructs only a market-data client — no broker, no trading client — so placing an order is not a capability it has, rather than a rule it has been told to follow. There is no order tool to prompt-inject around. Promoting a strategy to live trading is a manual step you take yourself, outside the agent entirely.

Worth knowing: everything Claude runs is journaled the same way your own commands are, and counts toward the same multiple-testing total. An agent that runs fifty backtests has genuinely made your next result harder to believe, and the tool will say so.

6. Where to go next


A word about expectations

Most things you try will not work, and the tool is built to tell you that quickly rather than slowly. That is the feature. A backtest that looks fantastic is the normal result of trying many configurations, and nearly every guardrail here exists to separate that from a real edge.

If a strategy survives walk-forward validation, clears the deflated Sharpe against everything you have tried, and still has positive expected return net of trading costs — then you have something worth a longer look.

warning

Educational software. Keep PAPER_TRADE=true unless you fully understand the consequences. Trading carries real financial risk, and nothing here is investment advice.