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Trade with institutional
discipline.

Get your entry, stop, and targets in seconds. Let the AI warn you when the math says to stay away.

1.5M+

patterns indexed

NYSE & NASDAQ

market coverage

< 3s

avg signal time

0.07 IC

institutional-grade signal

trading modes

Most traders lose money.

Not because the markets are rigged. Because they are trading without an edge.

No entry timing

Buying at the wrong price turns good picks into bad trades.

No stop discipline

Most traders don't know where their stop goes before they enter.

No pattern context

What worked last time in this exact setup? Nobody tracks that.

Paper trading mode, available on all plans

Test before you risk real money

Every signal includes a paper trade toggle. Run 30 days of paper trades, see your P&L, then go live with confidence.

How Riprop works — custom analysis workflow and automated market screening

For businesses

Power your platform
with Riprop

Hedge funds, brokerages, and fintech platforms can access institutional-grade trade intelligence with a single endpoint. No model training, no data pipeline.

✓1.5M indexed historical patterns
✓Real-time signal generation across 4 trading modes
✓Regime detection + pattern confidence scores
✓JSON response, easy to integrate

Sample API response

{
  "ticker": "AAPL",
  "mode": "day_trade",
  "market": "us",
  "timestamp": "2026-05-05T00:38:14.181394+00:00",
  "signal": {
    "direction": "long",
    "confidence": 0.6355,
    "regime": "bull_trend",
    "regime_confidence": 0.7374
  },
  "levels": {
    "entry_low": 275.12,
    "entry_high": 277.88,
    "stop_loss": 274.21,
    "target_1": 281.07,
    "target_2": 284.50,
    "risk_reward": 2.0
  },
  "position": {
    "size": 1,
    "risk_pct": 0.04
  },
  "pattern_analysis": {
    "match_rate": 0.70,
    "avg_return": -0.18,
    "similar_patterns_found": 10,
    "top_analogs": [
      { "ticker": "BSX", "date": "2022-07-26", "return_pct": 5.35, "similarity": 0.994 },
      { "ticker": "T",   "date": "2024-05-31", "return_pct": 0.89, "similarity": 0.994 },
      { "ticker": "LMT", "date": "2022-01-04", "return_pct": 0.08, "similarity": 0.993 }
    ]
  },
  "fundamentals": {
    "sector": "Technology",
    "news_sentiment": "bullish",
    "earnings_nearby": false
  },
  "risk_flags": [
    "Negative expectancy — 70% winners but avg return is -0.2%."
  ]
}

For individuals · Web app