Context7
by upstash
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud★ 64,224MITUpdated 3 days ago
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Quant Research
Overview
Description from the maker (original English)
Crypto backtest statistics: the server owns the trial count, so it cannot be understated.
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- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs in the cloud
- Transport: Streamable HTTP
- Category: Search & AI memory
- You need to fill in: Authorization
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- Runs on the maker’s server: what you ask it goes through their server.
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You need
- Runs in the cloudNothing to install; it connects over the internet
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Pick your app above. We remember it, so next time the button says “Install in …”.
This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
Run this in your terminal
claude mcp add --transport http quant-research https://quant-mcp.avasis.ai/mcp --header 'Authorization: your-value'Fill in the empty values before running it.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"quant-research": {
"url": "https://quant-mcp.avasis.ai/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
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Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"quant-research": {
"type": "http",
"url": "https://quant-mcp.avasis.ai/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
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What it can do
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Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
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Compared with others in Search & AI memoryi
- More stars than 0% of the other 7240 servers in this category
- Published to the registry more recently than 36% of the other 7240 servers in this category
- Basic checks equal to or better than 36% of the other 7240 servers in this category
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Common questions
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What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
by DeusData
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Runs on your computer★ 48,148MITUpdated 20 days ago
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Exa
by exa-labs
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud★ 5,213MITUpdated 44 days ago
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quant-integrity
Statistical tests for whether a backtest means anything.
**This library will not find you a profitable strategy. Its function is to reject
them.** Everything here exists to answer one question: given how many
configurations you tried before reporting this one, does the result survive?
pip install quant-integrityThe problem, in one picture
A moving-average crossover on BTC-USD hourly bars. After sweeping parameters, the best configuration returned an annualised Sharpe of 0.95 — and a Probabilistic Sharpe of 0.86, which reads as *"probably beats zero"*.
That is where most backtests stop. Here is the same result next to the bar that 28 attempts actually sets:
The best of 28 tries is large even when every underlying edge is exactly zero. Against that bar the deflated Sharpe is 0.157. The observed result is not marginal; it is below half of what luck alone produces.
A high PSR with a low DSR is the signature of an overfit search, not a borderline edge.
What the search was actually selecting on
The cause was visible in the trials themselves. Sorting the 28 recorded runs by trade count against Sharpe:
Every configuration that traded less looked better, at ρ = −0.96. That is not a signal being discovered — it is cost drag being measured, plus a slow moving average approximating buy-and-hold in a rising market. The search was not finding an edge. It was finding the configuration that traded least.
And the interval was never tight to begin with
Reported alone, Sharpe = 0.95 implies a precision the data does not support.
Every number above is reproducible
git clone https://github.com/abhayjnayakk/quant-integritycd quant-integrity && pip install -e . python docs/make_figures.py # regenerates all three figures and prints the values
| Trials evaluated | 28 |
| Sharpe (annualised, after costs) | 0.9500 |
| Probabilistic Sharpe (PSR) | 0.8556 |
| E[max Sharpe] under the null, 28 trials | 1.8525 |
| Deflated Sharpe (DSR) | 0.1568 |
| Bootstrap 95% CI on Sharpe | [−0.93, 2.69] — straddles zero |
| Excess kurtosis | 18.61 |
| Spearman ρ(Sharpe, trades) | −0.9618 (p = 3.8e−16) |
| Minimum backtest length | 1.94 years required, 1.25 available |
The trial count is taken from the recorded runs rather than typed in. An earlier version of this page hardcoded 29 while the file recorded 28, and quoted a deflated Sharpe from one and a minimum backtest length from the other.
The figures and this table are generated by the same script from examples/btc_ma_runs.npz (24 KB, the actual return series and per-trial results). No server and no account. There is also a longer walkthrough in examples/btc_ma_postmortem.ipynb.
**On one number that is *not* in this repo.** The same specification was later
run across a basket rather than a single asset, and pooled to a Sharpe of
−1.376 over 10 assets with 3 of them positive. That result lives in the hosted
server's ledger, not in this repository — the example file here carries only the
single-asset trials, so you cannot reproduce the basket figure from it. It is
quoted here because it is the strongest part of the story, and flagged because
a number you cannot check should always be labelled as one.
What's in it
| Function | Question it answers |
|---|---|
deflated_sharpe(returns, n_trials) | Does this Sharpe beat what the best of N trials produces by luck? |
probability_backtest_overfitting(matrix) | Does your selection procedure carry information at all? |
combinatorial_purged_cv(...) | Cross-validation splits with purging AND embargo |
stationary_bootstrap_ci(returns) | How wide is the interval really? |
reality_check(family, benchmark) | Is the family's best member better than the benchmark it was chosen over? |
effective_tests(matrix) | How many INDEPENDENT tests does a correlated basket give? |
min_backtest_length(n_trials, ...) | Is your sample even long enough for the search you ran? |
triple_barrier_labels(...), dollar_bars(...) | Labelling and activity-based sampling |
spec_hash(spec) | Content-addressed strategy identity |
verify_attestation(record, key) | Independently check a signed research record |
import quant_integrity as qiout = qi.deflated_sharpe(returns, n_trials=28) print(out["dsr"], out["psr"])
n_trials means every configuration you evaluated, including the ones you discarded. Understating it produces a flattering answer. Nothing in this library can check it — which is exactly the problem the next section is about.
Commercial intent, stated on day one
The statistics in this repository are AGPL-3.0 and free forever. Every method here is published academic work. There is no moat in the arithmetic and no version of this library is crippled to sell you an upgrade — a teaser would fail as a funnel and would forfeit the only thing an integrity tool has.
Hosted attestation is a paid service. Verification is public and lives here. *Issuance* is not, for the reason below. Saying this up front so nobody can later claim a rug-pull.
Why self-hosting can't attest
A trial count is only meaningful if it cannot be revised downward. But if you run your own ledger, you can edit it. Not through malice, usually — through the ordinary temptation to restart the count after a rewrite, or to not record the sweep that went nowhere. A self-attested integrity record certifies nothing, because the person attesting is the person who benefits.
Verification is public. Issuance is the service.
Anyone can check an attestation with this library and an issuer's public key. No cooperation from the issuer is required, and none of the checking code is withheld. What you cannot do is *make* one, because a signature is only worth something when the signer is not the beneficiary.
The property that does the work is not any single signature — it is the chain. verify_chain confirms that, across a sequence of attestations:
- sequence numbers are consecutive, so no record was removed from the middle;
- each references the previous record's digest, so none was altered afterwards;
- `n_trials` never decreases.
An outside party can establish all of that without ever seeing the ledger. Someone who edits their own records cannot reproduce it, because they would have to re-sign every subsequent record with a key they do not hold.
That is the whole business: not the arithmetic, which is here and free, but operating an instance that has no stake in the answer. That instance is quant.avasis.ai — an MCP server, so the research runs inside Claude, Cursor, VS Code or any MCP client, and the trial count is kept by the server rather than by the conversation.
Scope
This is a statistical instrument. Deliberately absent, and staying absent:
- No buy, sell or hold signals. No target prices, position sizes or stop levels.
- No model portfolios or allocations.
- No claims about returns, and no examples implying any.
The library takes return series and trial counts and returns statistics about them. It does not know what you are trading and does not offer an opinion on it.
Install and contribute
pip install quant-integrity # statisticspip install "quant-integrity[attestation]" # + signature verification
Python 3.10+. Depends on numpy, scipy and pandas. Fully typed (py.typed).
Contributions require a CLA and a DCO sign-off — see CONTRIBUTING.md, which explains why without apology.
Licence
AGPL-3.0-or-later. See LICENSE and NOTICE.
The network-use clause is deliberate: it means a competitor cannot run a closed hosted fork of this code. It does not restrict you from using the library in your own research, hosted or otherwise, without publishing anything.