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Trading Intelligence

Top Machine Learning Models - 100% Unlimited Usage
Discover new strategies and signals to gain an advantage.
100% private - Your data STAYS on your device.

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Wise Apple dashboard showing a VOO candlestick chart with an 82% win rate trade overview box
Wise Apple dashboard showing an SPY candlestick chart with a 100% win rate trade overview box
Wise Apple dashboard showing a TLSA candlestick chart with a 90% win rate trade overview box
Wise Apple dashboard showing an ETH candlestick chart with a 61% win rate trade overview box
Green markers mean profit. Red markers mean losses.

Profit is a rough estimate, NOT actual profit — no fees were used in these tests shown above.

1M+ Strategies Tested
0 Years of Historical Data
~20 ML Models
$0 To Start

Build it. Train it.
Read the evidence.

Four honest steps — from a point-and-click model, to a transparent report you can read, to a live alert the moment you choose your setup.

01 Build a PowerCore

Point, click, build a Machine Learning strategy — zero code

Assemble a strategy template — a “PowerCore” — in a visual studio. Engineer your own features, filters and targets, then pick from roughly twenty supervised model types: gradient-boosted trees, random forests, SVMs, a Lorentzian nearest-neighbor classifier, ensemble voting.

  • ~20 model types, not just a backtester — tunable similarity metrics & ensemble voting
  • Custom feature engineering by point-and-click — no pandas, no notebooks
  • Pro-grade exits & sizing: chandelier/ATR trailing, time-based exits, Kelly + volatility-scaled sizing
PowerCore Studio — Basic Info tab of the Starter · Scout template showing the model, initial capital, and fee settings
PowerCore Studio — Targeting Plan with ternary and binary labeling plus direction, length, and source controls
PowerCore Studio — model configuration for Scout I with model selection, feature scaling, and training schedule
PowerCore Studio
02 Train Your Model

Train on full history — then run it again, and again

Train against a managed market-data corpus right on your machine. No GPU quota. No “runs left.” Iterate as many times as you want — nothing about a run costs us money, so it doesn’t cost you a credit.

  • Test on full history — the bar-depth wall is gone
  • Unlimited local model runs — never watch a counter again
  • Launch focus: stocks + crypto, the markets you actually trade
Training a model locally across full daily market history with entry and exit markers on the chart Local engine
03 Read the evidence

Deep, transparent reports — see why it works or doesn’t

Every run produces a deep evidence report: equity curve, trade-by-trade journal, win-rate / drawdown / Sharpe, time-of-day analysis, candle-by-candle replay. Accuracy, precision, recall, F1. Export to CSV or PDF for your own records.

  • Honest backtests: market-impact (Almgren-Chriss), tiered fees, per-candle volume cap, explicit slippage
  • Trade journal + candle replay — verify the logic instead of trusting it
  • Scheduled Radar scans produce shareable evidence bundles (radar.wiseapple.com)
Deep evidence report — single-model run with metrics, trade overview, and a trade-by-trade journal across 585 candles
Deep evidence report — ensemble run combining multiple models with per-model win rate and profit
Deep evidence report — model metrics including score, precision, recall and F1 with trade overview and running stats
Evidence report
04 Set an alert

Found a setup you like? Get pinged when it happens live

Once a PowerCore proves itself, attach it to an alert. Your own Alert Node watches live markets candle-by-candle and notifies you the moment your strategy signals an entry or exit — running on your hardware, not our cloud.

  • Attach any trained PowerCore — alerts fire on live enter / exit signals at 1H, 4H, 1D or 1W
  • Reach you your way: email, SMS, webhook, or in-app — stack multiple channels per alert
  • Runs on your own Alert Node — no cloud middleman, watch many symbols at once
Create alert dialog on ETH-USD — choose Alert Node, PowerCore, period, message, and notification channels
Right-clicking ETH-USD on the chart to add a live alert, with the Alerts panel listing active alerts on the right
Live notifications feed showing BTC-USD entry and exit signals fired by an attached strategy
Alert Nodes manager showing your own Main node — health, version, and reinstall or roll back controls
Live alerts
Model bench

Supervised ML,
scored four ways.

Roughly twenty model types you can mix, vote, and measure — judged on classification metrics, not gut feel.

GBT Gradient-Boosted Trees XGBoost & LightGBM engines
RF Random Forests Bagged decision-tree ensembles
DT Decision Trees Transparent, rule-based splits
SVM Support Vector Machines Linear, RBF, polynomial & sigmoid kernels
kNN k-Nearest Neighbors Incl. a Lorentzian-distance classifier
ENS Ensemble Voting 7 consensus schemes, incl. ranked-choice

≈20 supervised model types in all — mix and match, then let them vote.

The four ways every model gets graded
Accuracy
Share of calls it gets right
Precision
How clean its buy/sell signals are
Recall
How many real moves it catches
F1
Precision & recall, balanced
1–100Composite score Accuracy is only half the test. Every model is also scored on real trade economics — then rolled into one score you can rank the whole bench by, alongside ROC-AUC across every threshold. Win rate Realized ROI Profit / loss Max drawdown

Your strategy shouldn’t live on
someone else’s servers.

We got tired of trading “research” tools that meter your curiosity, count your experiments, and quietly keep a copy of the ideas you sweat over. So we built the opposite: a no-code machine-learning studio that runs on your machine — the heavy modeling happens on your hardware, and your work stays on your device unless you choose to export it.

No cloud meter No code No card to start

Build a strategy, train ~20 ML models locally, and read the evidence for yourself — research like a quant without coding like one.

Your hardware. Your edge.

Your move

Everything in one studio.

Any questions?

Research like a quant —
without coding like one.

Quant-grade modeling, secretly used behind black boxes of hedge funds and hard-to-understand walls of code — it’s now yours, within a few clicks!

Build real models, test your ideas, and read the hard evidence, no code required. Serious research firepower, finally open to anyone.

Start Now
Rigor & risk

Guardrails that keep you
honest with yourself

The fastest way to lose money is a backtest that lied to you. Wise Apple builds in the anti-overfitting checks most prosumer tools skip.

Look-ahead checks
Automated guards catch features that "peek" into the future before it would have happened.
Incomplete-candle trimming
The forming bar is dropped from training, so your model never learns from data it couldn't have had.
Label-horizon embargo
A gap between train and test prevents label leakage across the boundary — a classic silent killer.
Train-only PCA
Dimensionality reduction is fit on training data only, so test results aren't quietly contaminated.
Triple-barrier + meta-labeling
Modern label construction frames trades as take-profit / stop / timeout, then meta-labels for quality.
Execution realism
Market impact, fees, slippage, and volume caps are part of the test — not an afterthought.

The five things, in one place.

Plenty of tools do one or two of these. The combination — all five at once — is what we couldn't find anywhere, so we built it.

  • A no-code GUI ML studio, so you don't have to become a programmer.
  • Custom feature engineering — your inputs, filters, and targets.
  • Full-corpus training, not a sample-sized teaser.
  • Client-side, unmetered compute on your own hardware.
  • Managed market data, so you skip the cleaning and wrangling.
  • Local-first by default — data sovereignty isn't a setting, it's the design.

And here's the part we're proudest of: we meter the data plan, not your model experiments. Competitors have to cap your runs because every run costs them money. Your machine doesn't send us that bill — so we don't send it to you.

Read this first

This is for one kind of trader.

You trade by rules, not gut. You've got real capital on the line. You're "Python-curious" — but you are not going to burn 200 hours wrestling Backtrader bugs just to test one idea. You want to research like a quant without coding like one.

If that's you, keep reading. If you want push-button "buy now" signals to blindly follow, this isn't your tool — and we'll be honest about that on every line.

Which trader
are you?

Four roads into the same studio. Pick the frustration that sounds like yours — jump straight to how Wise Apple fixes it.

“I ran out of bars again — I can’t test on more than a couple years of data.” The charting trader “I’d spend months learning the framework instead of testing my idea — I’m done coding my own backtester.” The Python-quitter “Why upload my strategy to someone else’s server, then wait in a queue to test it?” The privacy trader “It’s easy to make a backtest look good — but in-sample numbers are basically worthless once slippage and fees hit.” The no-code rigorous trader
For the TradingView / charting trader

“I ran out of bars AGAIN. I can’t test my idea on more than a couple years of data.”

Your edge deserves more than 3 years of data.

Cloud charting tools cap bar depth by plan and meter compute per run. You end up stress-testing a position idea on a fraction of the history it needs. Wise Apple trains on a managed market-data corpus on your machine — so a regime from years ago is still in the sample, not paywalled out of it.

  • Full history with no per-plan bar limit on backtests
  • Unlimited local runs — no “100 runs / month” counter
  • Equity curves, trade journals, and candle replay to inspect every fill
Test on full history — free Email only. Upgrade later for deeper data — not for permission to keep testing.
Backtest on full BTC-USD price history with model trade entry and exit markers — no bar limit No bar caps · full-corpus training
For the trader who quit coding a backtester

“Every backtesting framework I looked at had an insurmountable amount of context just to grok the concepts. I’d spend months learning the machinery instead of testing my idea.”

Done coding your own backtester?

The block was never your intelligence — it was time. Either you learn Python and a framework, pay for an institutional platform, or settle for a scripting box. None of those let you just map out an idea and see how it held up. PowerCore Studio is the research engine you were trying to build: point-and-click feature engineering, model selection, and training — no framework to grok, no dependency hell, no DataFrame errors to debug.

  • Zero-code studio — combine indicators and test them properly
  • ~20 supervised ML model types, picked from a menu — not a repo
  • Portable PowerCore templates you save, load, and reuse
Skip the framework — start free No install. Opens on your device.
PowerCore Studio — picking an ML model from a menu, no code No-code · ~20 ML model types
For the trader who keeps their edge to themselves

“Why would I upload my strategy to someone else’s server — then pay them and still wait in a queue to test it?”

Your strategy stays on your machine.

Wise Apple is local-first by design. The ML training runs on your own device through a custom-built engine, and your PowerCore templates stay with you. There’s no run quota gating your research — and because the engine runs client-side, your experiments never sit behind someone else’s GPU queue.

  • Local-first compute — your hardware does the training, not a rented cloud node
  • Portable PowerCore templates and reports you export and keep — no lock-in
  • Pricing meters data depth, not how often you’re allowed to test
Keep it on your machine — free If we ever shut down, your local templates and exports are still yours.
Your machine Private

ML training runs here. Your PowerCore templates stay here.

vs
Their cloud Uploaded

Sees your strategy. Meters your runs. Waits in a shared GPU queue.

0 uploads · ∞ local runs · 100% your machine
For the no-code trader who won’t trust a pretty backtest

“It’s easy to make a backtest look good — in-sample results are basically worthless, and once you add slippage and fees a ‘great’ strategy can turn into a money-loser.”

Real ML on your trades — without writing Python.

You know the failure mode: a model that looks great because future knowledge leaked in, then dies the moment fills and fees get real. Wise Apple is built to stress-test instead of cherry-pick — leakage guardrails help keep lookahead bias out, and execution-realism modeling prices in slippage and costs — so the numbers you read are less likely to evaporate in forward testing.

  • Anti-overfitting leakage guardrails to reduce lookahead bias
  • Honest backtests with execution realism, not idealized fills
  • Deep evidence reports: win-rate, drawdown, Sharpe, trade-by-trade
See the evidence — start free Backtested / historical results don’t predict future performance.
PowerCore evidence report — model metrics (precision, accuracy, AUC) and trade overview (win-rate, profit, ROI) with trade-by-trade detail Leakage guardrails · deep reports

See exactly what
the model knows.

Build features visually. Train locally. Compare the evidence. Start free with just an email — no card, no install, unlimited local runs.

Create free account

Research & analytics software — not financial advice. Backtested / historical results don't predict future performance. Unlimited local runs; data depth depends on your plan.

Anti-overfitting guardrails,
on by default

Leakage is the quiet killer of systematic strategies. Wise Apple applies the same discipline a careful quant would — automatically, on every run, before you ever see a result.

Leakage guards

The future never leaks into the past

  • Per-row look-ahead checks. Each feature row is validated so it can only ever use information that existed at that point in time.
  • Incomplete-candle trimming. The still-forming bar is dropped, so a half-printed candle can't sneak its outcome into training.
  • Label-horizon embargo. A gap is held out around each label window so the train and test sets can't overlap in time.
  • Train-only PCA fit. Dimensionality reduction is fit on training data alone, then applied forward — never peeking at the test fold.
Labeling method

Triple-barrier + meta-labeling

Instead of naively labeling "did price go up," Wise Apple uses a triple-barrier method: each trade is judged against an upside target, a downside stop, and a time limit — whichever is hit first. It's the same event-based labeling scheme quants use to keep targets honest.

A second meta-labeling pass then learns when to trust the primary signal, separating "should I act" from "which direction." The result is a model that's evaluated on realistic outcomes, not on a flattering definition of success.

Determinism and embargo handling are continuously checked, because a guardrail you can't reproduce isn't a guardrail.

Honest backtests model
the cost of trading

A fill at the perfect price with zero friction is fiction. Wise Apple's backtest engine charges your strategy what the market would — so the equity curve you read is one you could have plausibly lived through.

Market impact

Almgren–Chriss impact model

Large orders move the market against you. Wise Apple models that slippage with the Almgren–Chriss framework, so size has a cost — exactly as it does in the real book.

Liquidity

Per-candle volume cap

You can't trade more than the market traded. A per-candle volume cap prevents backtests from filling phantom size that no real order book could absorb.

Costs

Tiered fees

Commissions are applied on a tiered schedule rather than a flat hand-wave, so the drag of frequent trading shows up honestly in the bottom-line metrics.

Friction

Explicit slippage

Slippage is modeled explicitly instead of assumed away — the gap between the price you wanted and the price you'd realistically get is part of every result.

Built-in guardrails

A pretty backtest that leaks
is just a lie with a chart.

Most DIY backtests fool you with look-ahead leakage. Wise Apple ships the anti-overfitting safety most coders forget to write — automatically.

Look-ahead checks

Per-row look-ahead detection stops your model from "seeing" the future it's supposed to predict.

Incomplete-candle trim

The half-formed current candle gets trimmed, so results aren't inflated by data that didn't exist yet.

Label-horizon embargo

An embargo gap around each label-horizon prevents train/test bleed across the prediction window.

Rolling out-of-sample tests

Models are scored on rolling future windows they never trained on — an edge has to survive data it has genuinely never seen.

Look-ahead-safe scaling

Feature scaling uses only past and present values, never future rows — so normalizing the data can't quietly smuggle in look-ahead.

Train-only PCA fit

Dimensionality reduction is fit on training data only — never peeking at the test set.

Triple-barrier + meta-labeling

Modern labeling (triple-barrier method, meta-labeling) for cleaner targets and fewer false signals.

Market-impact pricing

Order size pays a price penalty scaled to the bar's volume — large fills cost more, exactly as they would in a real book.

Per-candle volume cap

You can't fill more than the bar actually traded — phantom size no real order book could absorb is blocked.

Tiered fee schedule

Commissions follow a tiered, volume-based schedule, so the real drag of frequent trading lands in the bottom-line numbers.

Reproducibility checks

Runs are hashed and parity-tested, so a result you can't reproduce gets flagged instead of trusted.

Full honest scorecard

Every run reports ROI, drawdown, win-rate, fees and the equity curve out-of-sample — not one flattering number.

Research & analytics software — not financial advice. Backtested / historical results don't predict future performance.

Local-first by design

Your edge is yours.
It stays on your machine.

Cloud platforms upload your best ideas to their servers. Wise Apple runs the heavy ML locally — desktop plus a web-based, custom-built engine. Your configs, models and reports stay on your device unless you choose to export or share them.

  • Heavy ML compute runs client-side, on your hardware
  • Strategies & reports stored locally on your device
  • You own your work — export, import & share on your terms
  • Versioned, backwards-compatible PowerCore templates + local backups
What "ownership" actually buys you

Three freedoms
no cloud tool gives you

Freedom to experiment

Run the same idea fifty different ways tonight. No counter, no credits, no "you've hit your limit" wall mid-thought.

Freedom over your data

Your edge isn't uploaded to someone's training set. Models and reports live on your device, on your terms.

Freedom to take it with you

Versioned, backwards-compatible PowerCores with local backups. Export, import, and keep your work — no lock-in.

Before & after

From testing blind to knowing your edge

You already think in systems. What you've been missing is a way to test them honestly — without a bar limit, a black box, or 200 hours of Python. Here's the shift.

Before

Where most systematic traders are stuck

  • Testing ideas blind. Twenty hypotheses in a notes file you've never properly backtested.
  • Slamming into bar limits. A few months of intraday history — nowhere near enough to test across regimes.
  • Trusting a black box. "AI signals" you can't open up, can't verify, and can't reproduce.
  • Quitting on Python. An afternoon lost to a Backtrader DataFrame error before you tested a single idea.
  • Curve-fitting by accident. The prettier the equity curve gets, the less you trust it — and you're right not to.
After

Where you're headed

  • Building ML strategies visually. Point, click, and engineer features and targets — no code, no framework.
  • Training on a decade, not a weekend. Run on full history with no bar limits, as many times as your curiosity demands.
  • Reading honest evidence. Equity curves, trade journals, win-rate, drawdown, Sharpe — the real story, not a sales pitch.
  • Catching curve-fits early. Built-in leakage guards stop fake edges before they ever reach your live account.
  • Knowing before you risk money. You sit down to a setup already understanding whether the evidence supports it.
For the trader who is done guessing

Know which setups have edge
before you trade them.

Ready to find
your trading edge?

Join thousands of traders using AI to backtest, optimize, and trade smarter — all from your device.

Try It Now