·under the hood

How AlgoVault Works

The Trading Model API.

Self-Tuning Quantitative Machine Learning.
Published track record, Merkle-anchored on Base L2.

Built for Autonomous AI agents.

363,611verified calls
91.6%PFE win rate
1240+assets
12venues
MODEL · TRADE_CALL_APILIVE
Binance logoBinance
Hyperliquid logoHyperliquid
Bybit logoBybit
OKX logoOKX
Bitget logoBitget
MODEL
AlgoVault
Batch 95
VERDICT
callBUY
conf78
regimeTRENDING_UP
assetBTC · 1h
sig_3f1c…d28a · anchored on Base L2merkle proof ✓
·composite verdict

What AlgoVault is

· one call · one number
Composite verdict

Multi-factor scoring across momentum, trend, derivatives, OI, and volume. Direction + confidence + regime in one call.

· five venues · one model
Cross-venue intelligence

Same model evaluates Binance, Hyperliquid, Bybit, OKX, Bitget. 1240+ assets. 11 timeframes.

· merkle anchored
On-chain proof

Every call hashed at emission, anchored on Base L2 daily. 95+ Merkle batches published.

·self-tuning model

How the model improves

AlgoVault's composite verdict is produced by a self-tuning quant ML model.

The Autonomous Optimization Engine (AOE) continuously tunes the model from published trade outcomes — each call adds to the dataset, the dataset feeds the model, the model gets sharper. Our PFE win rate has improved monotonically since launch — verifiable in every Merkle batch on Base L2.

01
STEP 01
Agent calls get_trade_call
02
STEP 02
Outcome lands in the dataset
04
STEP 04
Next call uses the sharper model
03
STEP 03
AOE updates model weights
LOOP
PFE WR OVER TIME
Every batch publishes the next data point. No retroactive edits. Improvement is measured against the public Merkle log on Base L2.
View Merkle batches on Basescan →
·integration

How agents call it

MCP tools is the contract. Discover what's callable, call it. No client library required.

agent.jsMCP
// Any MCP client (Claude Desktop, Cursor, Cline, Codex, …)
const verdict = await mcp.call("get_trade_call", {
coin: "BTC",
timeframe: "1h"
});
// → { call: "BUY", confidence: 78, regime: "TRENDING_UP", ... }
· Drop-in matrix
Works with any MCP client
Claude DesktopClaude CodeCursorClineCodexWindsurfContinue.dev
Plus every MCP-spec-compliant client.
·the infrastructure decision

You don't train your own GPT.
Why train your own trading model?

· build it yourself
2+ engineering-years
  • 5+ exchange adapters (REST + websocket)~3–4 weeks each
  • Multi-factor scoring engine~3-6 months
  • Outcome tracking pipeline~1-3 month
  • Track-record verification~12-24 months
  • Self-tuning weight loopopen-ended
Total: ~2+ engineering-years before you ship one verdict
or
· Call Algovault
One MCP call. Today.
  • ✓One MCP call from any agent clienttoday
  • ✓1240+ assets · 11 timeframes · 12 venueslive
  • ✓363,611+ verified calls · 91.6% PFE WRpublished
  • ✓Self-tuning model (AOE) in productionshipping
  • ✓Merkle-anchored on Base L2every day
Total: npm install crypto-quant-signal-mcp — or zero install via remote MCP
·why we sell calls

If the model works,
why sell it?

AlgoVault is infrastructure, not an alpha bet. We sell access to a trading model the same way OpenAI sells access to GPT — the model itself is the asset, and per-call access is the revenue.

Plug your agent into the trading model behind the calls.

100 free calls/month. No signup required.
·under the hood

How AlgoVault Works

The Trading Model API.

Self-Tuning Quantitative Machine Learning.
Published track record, Merkle-anchored on Base L2.

Built for Autonomous AI agents.

363,611verified calls
91.6%PFE win rate
1240+assets
12venues
MODEL · TRADE_CALL_APILIVE
Binance logoBN
Hyperliquid logoHL
Bybit logoBY
OKX logoOK
Bitget logoBG
MODEL
AlgoVault
Batch 95
VERDICT
callBUY
conf78
regimeTRENDING_UP
assetBTC · 1h
sig_3f1c…d28a · anchored on Base L2merkle ✓
·composite verdict

What AlgoVault is

· one call · one number
Composite verdict

Multi-factor scoring across momentum, trend, derivatives, OI, and volume. Direction + confidence + regime in one call.

· five venues · one model
Cross-venue intelligence

Same model evaluates Binance, Hyperliquid, Bybit, OKX, Bitget. 1240+ assets. 11 timeframes.

· merkle anchored
On-chain proof

Every call hashed at emission, anchored on Base L2 daily. 95+ Merkle batches published.

·self-tuning model

How the model improves

AlgoVault's composite verdict is produced by a self-tuning quant ML model.

The Autonomous Optimization Engine (AOE) continuously tunes the model from published trade outcomes — each call adds to the dataset, the dataset feeds the model, the model gets sharper. Our PFE win rate has improved monotonically since launch — verifiable in every Merkle batch on Base L2.

01
STEP 01
Agent calls get_trade_call
02
STEP 02
Outcome lands in the dataset
04
STEP 04
Next call uses the sharper model
03
STEP 03
AOE updates model weights
PFE WR OVER TIME
Every batch publishes the next data point. No retroactive edits. Improvement is measured against the public Merkle log on Base L2.
View Merkle batches on Basescan →
·integration

How agents call it

MCP tools is the contract. Discover what's callable, call it. No client library required.

agent.jsMCP
// Any MCP client (Claude Desktop, Cursor, Cline, Codex, …)
const verdict = await mcp.call("get_trade_call", {
coin: "BTC",
timeframe: "1h"
});
// → { call: "BUY", confidence: 78, regime: "TRENDING_UP", ... }
· Drop-in matrix
Works with any MCP client
Claude DesktopClaude CodeCursorClineCodexWindsurfContinue.dev
Plus every MCP-spec-compliant client.
·the infrastructure decision

You don't train your own GPT.
Why train your own trading model?

· build it yourself
2+ engineering-years
  • 5+ exchange adapters (REST + websocket)~3–4 weeks each
  • Multi-factor scoring engine~3-6 months
  • Outcome tracking pipeline~1-3 month
  • Track-record verification~12-24 months
  • Self-tuning weight loopopen-ended
Total: ~2+ engineering-years before you ship one verdict
or
· Call Algovault
One MCP call. Today.
  • ✓One MCP call from any agent clienttoday
  • ✓1240+ assets · 11 timeframes · 12 venueslive
  • ✓363,611+ verified calls · 91.6% PFE WRpublished
  • ✓Self-tuning model (AOE) in productionshipping
  • ✓Merkle-anchored on Base L2every day
Total: npm install crypto-quant-signal-mcp — or zero install via remote MCP
·why we sell calls

If the model works,
why sell it?

AlgoVault is infrastructure, not an alpha bet. We sell access to a trading model the same way OpenAI sells access to GPT — the model itself is the asset, and per-call access is the revenue.

Plug your agent into the trading model behind the calls.

100 free calls/month. No signup required.
Answer guides
Build a crypto trading agent in Python →Claude-compatible crypto trading research stack →Build vs buy a trading model →Crypto market-regime detection API →Composite cross-exchange trade calls →Cross-venue funding-rate arbitrage →