CryptoGuard
Crypto risk scanner that detected every major crash in backtesting — 27 days early, on average.
Scan any token by name, ticker, or contract address. Get a deterministic verdict: PROCEED / CAUTION / BLOCK.
Version: 0.6.0 | Live API: https://gpartin--cryptoguard-api-fastapi-app.modal.run | Free tier: 5 calls/day | MCP: 7 tools
Backtest Results
Tested against 7 historical crypto crashes (LUNA, FTX, Celsius, 3AC, UST, SOL/FTX, TITAN) and 4 calm-market control periods. Full methodology and data: CryptoGuard backtest.
| Method | Recall | Avg Lead Time | False Positive Rate |
|---|---|---|---|
| CryptoGuard (WaveGuard engine) | 100% (7/7) | 27.4 days | 6.1% |
| Z-score baseline | 100% (7/7) | 28.4 days | 29.9% |
| Rolling volatility | 86% (6/7) | 15.5 days | 4.0% |
5× fewer false alarms than statistical baselines with the same recall.
Example: FTX Collapse (November 2022)
On October 16, 2022, FTT was trading at $23.73. Z-score analysis saw nothing (score 1.20, PROCEED).
CryptoGuard flagged CAUTION (anomaly score 4.72). The next day it escalated to BLOCK.
23 days later, FTX collapsed. FTT fell 94%.
Install
pip install CryptoGuardClient
Quick Start
from cryptoguard import CryptoGuardClient
client = CryptoGuardClient()
# Validate a trade — primary use case
result = client.validate_trade("bitcoin", action="buy", amount_usd=1000)
print(result["verdict"]) # PROCEED / CAUTION / BLOCK
# Scan a token
scan = client.scan("solana")
print(scan["risk_level"])
# Rug pull check
rug = client.rug_check("solana", "0xabc123...")
print(rug["risk_score"])
# Check free tier remaining
print(client.free_tier())
Primary Endpoint
curl -X POST https://gpartin--cryptoguard-api-fastapi-app.modal.run/v1/validate-trade \
-H "Content-Type: application/json" \
-d '{"token": "solana", "action": "buy", "amount_usd": 500}'
First 5 calls/day are free. After that: $0.05/call via x402 USDC, or via RapidAPI.
MCP Integration (Claude Desktop / AI Agents)
CryptoGuard is an MCP server with 7 tools. Works with Claude Desktop, Cursor, or any MCP client.
Option 1: Remote HTTP (no install)
{
"mcpServers": {
"cryptoguard": {
"url": "https://gpartin--cryptoguard-api-fastapi-app.modal.run/mcp",
"transport": "http"
}
}
}
Option 2: uvx
{
"mcpServers": {
"cryptoguard": {
"command": "uvx",
"args": ["--from", "CryptoGuardClient", "cryptoguard-mcp"]
}
}
}
Option 3: pip install
{
"mcpServers": {
"cryptoguard": {
"command": "python",
"args": ["-m", "mcp_server.server"]
}
}
}
MCP Tools
| Tool | Description |
|---|---|
cryptoguard_validate_trade |
Validate a trade → PROCEED / CAUTION / BLOCK |
cryptoguard_scan_token |
Anomaly scan for any token |
cryptoguard_validate_trade_plus |
Premium stateless decision bundle with what-if analysis |
cryptoguard_counterfactual_trade |
What-if sensitivity analysis with tipping-point guidance |
cryptoguard_rug_check |
DEX pair rug pull risk assessment |
cryptoguard_search |
Search tokens by name/symbol/address |
cryptoguard_health |
Service health check |
All Endpoints
| Method | Endpoint | Price | Description |
|---|---|---|---|
| POST | /v1/validate-trade |
5 free/day, then $0.05 | Primary — single verdict for AI agents |
| POST | /v1/validate-trades |
5 free/day, then $0.05 | Batch validate up to 20 trades |
| GET | /v1/scan/{coin_id} |
5 free/day, then $0.05 | Single token anomaly scan |
| POST | /v1/portfolio/scan |
5 free/day, then $0.05 | Portfolio batch scan (up to 50 tokens) |
| GET | /v1/scan/{coin_id}/history |
5 free/day, then $0.05 | Historical self-comparison |
| GET | /v1/rug-check/{chain}/{pair_address} |
5 free/day, then $0.05 | Rug pull risk assessment |
| GET | /v1/dex/new-pairs |
5 free/day, then $0.05 | New DEX pair discovery |
| GET | /v1/dex/search |
5 free/day, then $0.05 | DexScreener pair search |
| GET | /v1/dex/pair/{chain}/{pair_address} |
5 free/day, then $0.05 | DEX pair details |
| GET | /v1/dex/token/{token_address} |
5 free/day, then $0.05 | DEX token info |
| POST | /v1/intel/validate-trade-plus |
$0.10/call | Premium stateless decision bundle |
| POST | /v1/intel/counterfactual-trade |
$0.08/call | What-if sensitivity analysis |
| GET | /v1/track-record |
Free | Historical prediction track record |
| POST | /v1/trend-detect |
5 free/day, then $0.05 | Physics-based trend detection |
| POST | /v1/regime-detect |
5 free/day, then $0.05 | Regime change detection |
| POST | /v1/correlation-scan |
5 free/day, then $0.05 | Anomalous correlation detection |
| POST | /v1/volatility-forecast |
5 free/day, then $0.05 | Physics-based volatility forecast |
| POST | /v1/spectral-scan |
5 free/day, then $0.05 | Hidden periodicity detection |
| POST | /v1/whale-detector |
5 free/day, then $0.05 | Whale activity detection |
| POST | /v1/momentum-pulse |
5 free/day, then $0.05 | Momentum pulse detection |
| GET | /v1/search?q=... |
Free | Search tokens by name |
| GET | /v1/free-tier |
Free | Check remaining free calls |
| GET | /v1/pricing |
Free | Pricing details |
| GET | /v1/stats |
Free | Service statistics |
| GET | /health |
Free | Health check |
| POST | /mcp |
Free | MCP endpoint (JSON-RPC 2.0) |
| GET | /mcp/tools |
Free | List MCP tools |
| GET | /v1/agent/tools |
Free | Agent tool catalog |
| GET | /v1/agent/mcp |
Free | MCP server metadata |
How It Works
- Resolves token input — CoinGecko ID, ticker symbol, or contract address (7 chains)
- Fetches live market data from CoinGecko + DexScreener
- Builds baseline from tier-matched peers (microcaps vs microcaps, large caps vs large caps)
- Extracts 10 time-series features per day (price ratios, volume dynamics, momentum, volatility)
- Runs anomaly detection — GPU-accelerated WaveGuard engine scores each token against its peer baseline
- Multi-check pipeline: peer scan + rug pull + history + CEX/DEX spread + concentration risk
- Returns verdict: PROCEED / CAUTION / BLOCK with anomaly scores and top contributing features
About the detection engine
CryptoGuard's core scanner is powered by WaveGuard, a general-purpose anomaly detection engine that uses GPU-accelerated wave simulations instead of machine learning. Your token's feature vector is encoded onto a 3D lattice and evolved through coupled wave equations. Normal data produces stable wave patterns; anomalous data produces divergent ones.
The advantage over statistical methods: WaveGuard captures non-linear interactions between features that simple threshold checks miss. This is why it flagged FTT 13 days before z-score analysis in backtesting.
No model training, no drift, no retraining. Deterministic for the same input.
Key Features (v0.6.0)
- Backtested: 100% recall on 7 historical crashes with 27-day average lead time
- Free tier: 5 calls/day per IP, no signup required
- 2 payment options: x402 USDC per-scan ($0.05) or RapidAPI plans
- Deterministic: Same input always produces same verdict
- MCP server: 7 tools for AI agent integration (stdio + HTTP)
- Python SDK:
pip install CryptoGuardClientwith typed exceptions - Physics Intelligence: 7 endpoints for trend/regime/volatility/spectral/whale/momentum analysis
- Intel endpoints: Premium validate-trade-plus ($0.10) and counterfactual-trade ($0.08)
- Contract resolution: Accepts name, ticker, or contract address across 7 chains
- Batch validation: Up to 20 trades or 50 tokens per call
- Rug pull detection: DexScreener-powered liquidity and holder analysis
- 30 REST endpoints: Complete crypto risk intelligence API
Pricing
| Tier | Cost | Limit | Auth |
|---|---|---|---|
| Free | $0 | 5 calls/day per IP | None |
| Per-scan | $0.05/call | Unlimited | x402 USDC micropayment |
| RapidAPI Basic | $0/mo | 500K requests | RapidAPI proxy key |
| RapidAPI Pro | $9.99/mo | 10K requests | RapidAPI proxy key |
| RapidAPI Ultra | $29.99/mo | 100K requests | RapidAPI proxy key |
Architecture
AI Agent / User
|
v
CryptoGuard API (Modal, stateless)
|-- MCP endpoint (7 tools, JSON-RPC 2.0)
|-- Auth: RapidAPI → API key → x402 (USDC) → Free tier
|-- Token resolution (name/ticker/address → CoinGecko ID, 7 chains)
|-- Market data (CoinGecko + DexScreener, cached)
|-- Physics Intelligence (trend, regime, correlation, volatility, spectral, whale, momentum)
|-- Intel (validate-trade-plus, counterfactual-trade)
+-- WaveGuard anomaly engine (GPU-accelerated)
License
MIT
Metadata
Release files for CryptoGuardClient 0.6.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cryptoguardclient-0.6.0.tar.gz | 17.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| cryptoguardclient-0.6.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 34.8 kB
Release files / cryptoguardclient-0.6.0.tar.gz
| Download URL | cryptoguardclient-0.6.0.tar.gz |
|---|---|
| Size | 17.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d324896b5875ed860011c24e41ff8a7178236ffbbeb88947f353c04b2b33d3e8
|
|
BLAKE2b-256 checksum How to use checksums |
f6dc3e7e71936f52226ad5dcd1b92b09c1486c828d6fa9d320dfd23b30f00ef3
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Mar 16, 2026.
Transparency logRelease files / cryptoguardclient-0.6.0-py3-none-any.whl
| Download URL | cryptoguardclient-0.6.0-py3-none-any.whl |
|---|---|
| Size | 17.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
1b3ebee0e76271998dcb7fa42cc6e30677344a08f59af577b04ad17fd0ac6b9d
|
|
BLAKE2b-256 checksum How to use checksums |
30dbd193dcc3fdabbd32f66b89ce6ce4fcd46d92c66e7de9d337da9abf3d266a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Mar 16, 2026.
Transparency log