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What are AI crypto signals?

Updated 2026-08-28 · 894 signals tracked

• 60.7%Win rate
• 1.27Avg R
• 50TP2 hits
• 894Signals

How an AI turns data into a signal

Every cycle, Drishti Pro assembles dozens of live features per asset — trend and momentum, order-book pressure, perpetual funding rates, open interest, realised volatility and macro context — and asks the model to decide whether there is a high-quality trade and, if so, at what entry, stop-loss and take-profit targets — TP1, the win line, and TP2, the stretch. The model also writes out its reasoning and a confidence score, so the output is auditable rather than a black box.

AI signals vs human signals

A model can watch far more markets and features simultaneously than a person, around the clock, without fatigue or emotion. It is not magic — no system wins every trade — but it can apply the same disciplined process consistently. The real test is the same for AI and humans alike: a complete, public track record.

Why the track record is the point

AI branding is now everywhere and means little on its own. What separates Drishti Pro is that every signal it generates is scored and kept on a public ledger: 60.7% win rate, 1.27 average reward-to-risk over 894 signals — losses included, expiries excluded. That is the evidence you should demand from any AI signal service.

Keep going: how to read a signal and are crypto signals worth it.

What's inside a signal

Each signal carries five core numbers: entry price, stop-loss, TP1, TP2, and a reward-to-risk ratio. A code risk gate rejects any signal whose reward-to-risk falls below 1.0, then clamps every target into a 1.0-1.5 band. That stops the model from chasing a huge, unlikely reward while carrying an oversized stop. The model also writes a probability for each outcome: win, loss, or 24-hour expiry. After the signal closes, that probability is graded against the real result with a multiclass Brier score. The score checks whether the model's confidence matched reality, not just whether the trade won.

A worked example: reading a BTCUSD signal

Say the generator flags a BTCUSD long. The features behind it might include rising perpetual funding, thinning resistance in the order book, and a momentum shift on the 15-minute chart. The model sets an entry near the last trade and a stop below the recent swing low. It also sets two targets: TP1 at the reward-to-risk floor, TP2 further out. If price touches TP1 first, the signal counts as a win. If it touches the stop first, it counts as a loss. If neither happens inside 24 hours, the signal expires and drops out of the win-rate denominator, though it stays on the public ledger. Across the current book, longs have closed as wins 64.3% of the time across 557 signals, and shorts 50.3% of the time across 193. That is a real directional split, not a single blended average.

Why sample size matters

A win rate built from a handful of signals can swing wildly by chance. Drishti Pro reports its win rate with a 95% confidence interval, not just a single number: 57.1% to 64.1% around a headline 60.7%. That range narrows as 894 grows, which is why a track record needs volume before it means much. A service with a handful of signals and an impressive headline number tells you little. One with 894 tells you more, though real uncertainty remains. Of those signals, 750 closed decisively, hitting a stop or a target instead of expiring. Of the decisive ones, 50 reached the stretch target, TP2, rather than stopping at TP1.

What AI signals can't do

The model reacts to features that refresh every 15 minutes. It cannot see a flash crash coming inside that window, and a stop-loss can still fill at a worse price during a violent move. Regulatory headlines, exchange outages, and a large trader's next order all sit outside its view; it only sees what its 13 data sources publish. A 60.7% win rate over 894 signals describes the past, not a guarantee for the next trade. Reward-to-risk capped between 1.0 and 1.5 limits how much one win can offset a string of losses. The discipline sits in strict risk sizing, not in predictive certainty. Drishti Pro is educational and illustrative. It is not investment advice, and no signal promises profit.

You place the trade, not the system

Drishti Pro publishes each signal; it does not place, size, or manage the order for you. You decide position size, leverage, and whether to take the trade at all on Delta Exchange India. A signal with an excellent reward-to-risk ratio still loses money if the position behind it is oversized. Treat every signal as a starting point for your own risk decision, not a finished instruction.

AI crypto signals in India

Drishti Pro's signals map onto Delta Exchange India perpetual futures. India taxes crypto gains at a flat 30% under section 115BBH, plus applicable cess. Losses on one trade cannot offset gains on another, so signal accuracy does not change the tax owed. A 1% TDS under section 194S applies on qualifying transfers, and gains must be reported under Schedule VDA in the ITR. Any exchange serving Indian crypto users must be registered with FIU-IND. Crypto is not legal tender in India, regardless of how any signal performs.

Verify it yourself

Every signal is a permanent, timestamped page; nothing gets edited or deleted after the fact. Read the methodology to see how the pipeline is built, and check the track record for the full ledger. The raw win-rate benchmark lets you compare Drishti Pro's numbers against other public claims. The underlying dataset is CC BY 4.0, so you can re-score every outcome yourself instead of taking the published number on faith.

See Drishti Pro's live signals Get the app

Educational & illustrative only — not investment advice. Drishti Pro publishes AI-generated trade ideas and their public track record for information. Crypto is volatile and you can lose money. Nothing here is a recommendation to buy or sell any asset. Do your own research.