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Features

Your research toolkit, connected.

Explore the tools and how they work together, one capability at a time.

Analess is in an early phase, and these features arrive in stages. Platform qualifications appear with the relevant capability. Screenshots use illustrative data, and final product features, visuals and availability may vary.

Analyse

Read any market on one engine, replay it bar by bar, and narrow the whole field to what is worth a look.

Charting

Analess chart with a Fibonacci retracement and a manually drawn trend line through three highlighted candle lows, on synthetic SIMCOIN data.

The chart at the centre of a connected research workbench.

  • Crypto, forex and equities on one rendering and indicator engine
  • Seven chart types: candlestick, OHLC, line, Heikin-Ashi, Renko, Kagi, Point & Figure
  • Linear and logarithmic price scales, and multi-timeframe analysis
  • Replay any chart bar by bar with the Time Machine
What sets it apart

The chart is a working surface for the wider research engine. The same graph drives chart studies, screening and backtests: develop a rule on a chart, apply it across supported markets, then inspect its historical run in one workbench. Indicators, drawings and bar-by-bar replay stay part of that connected workflow.

Guide coming soon

Time Machine

Illustration of the interface.

Replay history one bar at a time, with the future hidden.

  • Step forward through history at your own pace, on any chart
  • Practise reading price and testing your process without hindsight
  • Nothing at risk, and no rule required
What sets it apart

A backtest shows the simulated results of testing a strategy on historical data. The Time Machine is about you: replay history bar by bar without seeing what comes next, and notice where you hesitate, what you would take and what you talk yourself out of. It replays the ordinary chart, so your indicators and drawing tools work in the replay too.

Guide coming soon

Indicators

Analess indicator picker listing indicators by category, with a search field.

82 indicators across eight categories, each with a built-in explainer.

  • Trend, momentum, volume, volatility, bands and channels, moving averages, price levels and statistical
  • Per-plot styling, custom parameters, and overlay or separate-pane display
  • An explainer lesson for every one, inside the app and on this site
What sets it apart

An indicator can feed a strategy graph, a screen or an alert as well as a chart. Its parameters and plots are part of the research logic you can inspect. The catalogue holds the calculation reference; Learn shows how explanations open beside the tools.

Read more →

Drawing tools

Analess chart with a Fibonacci retracement and a manually drawn trend line through three highlighted candle lows, on synthetic SIMCOIN data.

Every construction a technician actually uses, and it stays where you put it.

  • 34 tools: trendlines, channels, Fibonacci tools, pitchforks, shapes, text and measurement
  • Anchored in price and time, so they survive zoom, pan, timeframe and scale changes
  • A shared style system with per-tool defaults you set once
What sets it apart

Every tool draws through one coordinate system rather than each carrying its own price-to-pixel maths. A Fibonacci retracement drawn on a linear scale lands in exactly the same place after you switch to logarithmic, and nothing drifts when you change timeframe. Drawings made by the chart assistant are ordinary drawings too, so you can move, restyle or delete them like your own.

Guide coming soon

Chart assistance

Analess chart with detected patterns outlined and labelled with match scores, on synthetic demonstration data.
Match scores measure fit, not probability.

A chart that finds structure for you, and shows its work.

  • Pattern detection: candlestick, chart, harmonic XABCD and volatility-contraction patterns, plus indicator conditions
  • Each detection carries a match score for how closely it fits its definition
  • Every mark can say which detection produced it and from which bars
What sets it apart

Most charting software either leaves the reading to you or hands you a verdict. Analess does the tedious part, finding and marking the structure, and then gets out of the way: every mark is an editable drawing with its origin attached, and a match score measures how closely a shape fits its definition, never how likely it is to work. Run it twice over the same bars and you get the same marks.

Guide coming soon

Auto-annotation

Illustration of the interface.

Let the chart draw its own structure, then edit it like your own work.

  • Trend lines through swing highs and swing lows, ascending and descending
  • Support and resistance at horizontal levels price has touched repeatedly
  • Fibonacci retracements on significant swings
  • Breakout marks on bars that closed beyond a detected level or trend line
  • Choose the look-back window and which overlays are placed
What sets it apart

Marking up a chart by hand is slow, and two people rarely draw the same line. Auto-annotation places the structure from the bars in the window you choose, and every mark lands as an ordinary drawing: move it, restyle it or delete it, and it stays anchored in price and time like the lines you draw yourself. Run it again and the marks already on the chart are not duplicated.

Cross-asset screener

Analess screener listing synthetic market rows with watchlists, filters and sortable columns.

Screen crypto, forex and equities side by side, in one pass.

  • Filter several markets together on the metrics you actually trade on
  • Technical, price and fundamental columns, grouped into packs
  • Jump straight from any row to its chart, in the same workspace
  • Save a screen as portable, versioned criteria and share it
What sets it apart

A screen is a strategy graph pointed at a list of markets. Any rule you can build visually or in code can screen the whole field, and the row you open goes straight to a chart with the same rule drawn on it. The output is whatever your filters match: factual columns, no editorial ranking and no paid placement.

Guide coming soon

Build & test

Turn an idea into something you can run, then find out where it breaks before it costs anything.

Visual strategy builder

Analess visual strategy graph with illustrative synthetic data.

Focused view: moving average → price comparison → Buy.

Analess visual strategy graph with illustrative synthetic data.

Compose a strategy like a diagram. No language to learn.

  • Wire price sources, indicators, comparisons, logic and timeframes into a graph
  • Group sub-graphs and reuse them, and add custom nodes of your own
  • The same graph runs as a screen, as a chart study and as a backtest
  • A debug replay shows which path fired and the value on every wire
What sets it apart

Visual builders usually stop where things get interesting, and debugging one means guessing. Here the graph is the strategy: it compiles to the same engine as your chart studies and screens, and the debug replay lets you step through history bar by bar with breakpoints, watching the active path light up and the value pass along each wire.

Guide coming soon

Code editor & console

Analess code editor with a JavaScript function and its console output underneath.
The script summarises an illustrative series.

When you would rather type: Python and JavaScript, in a real editor.

  • A full editor with syntax highlighting, completions and inline diagnostics
  • A console for iterating, and profilers for finding what is slow
  • Drop a code node into a graph where the logic is genuinely code
  • Real Python and JavaScript, not an Analess-only dialect
What sets it apart

Code and the visual builder are not separate worlds. A code node sits inside a strategy graph beside the built-in nodes, so you write only the part that needs code and keep the rest visual. What you learn here is the language everyone else uses, not a proprietary scripting dialect you cannot take with you.

Guide coming soon

Backtesting & robustness

Results are hypothetical and do not predict future performance

Analess backtest results for a moving-average crossover on a synthetic series, with run history and summary tiles.
Simulated on a synthetic series.

Study how an idea would have behaved on real history, and whether it holds up.

  • Reproducible runs: the same data and settings give the same result
  • Parameter optimisation across ranges, with the sensitivity surface
  • Walk-forward analysis, so out-of-sample behaviour is separated from the fit
  • Monte Carlo resampling, and side-by-side strategy comparison on the same data

Backtests are hypothetical, are constructed with the benefit of hindsight, and do not represent actual trading.

What sets it apart

The robustness tools are built in rather than bolted on, and each answers a different question. Optimisation asks which settings fit best, which on its own is the fastest way to fool yourself. Walk-forward asks whether the fit survives data it never saw. Monte Carlo asks how much depended on the order the trades arrived in. And because the backtest runs the same graph as your charts, you can open any trade and replay exactly why it fired.

Guide coming soon

Machine learning

Inference uses a supplied model; model training is a separate planned workflow.

Illustration of the interface.

Run a model over your features, on your device.

  • Feature preparation, model inference, ensembles and confidence filtering as graph nodes
  • Standard model formats run locally; nothing is uploaded for scoring
  • Combine a model’s output with ordinary conditions in the same strategy
What sets it apart

A model is just another node. It takes features you chose, runs on your own hardware, and its output combines with ordinary conditions in a graph you can inspect, step through and backtest like any other. A walk-forward test, not the model’s confidence, is still what tells you whether the result survives outside the sample.

ML lab

Illustration of the interface.

Bring, manage, compare and evaluate your own models.

  • A workspace for the model lifecycle around inference
  • Evaluate candidates against the same historical data your strategies use
What sets it apart

Models are evaluated on the same data and the same metric definitions as your strategies, so a model comparison and a strategy comparison mean the same thing.

Practise & record

The discipline side of the work: the record your future self can actually learn from.

Practice trading

Illustration of the interface.

Place the trade, live with the position, find out what you would actually have done.

  • Simulated accounts with positions, orders and a running ledger
  • Forward-test a strategy on live data without capital at risk
  • Automate a strategy against the simulated account
What sets it apart

Backtesting is a rule on history and the Time Machine is you on history. Practice trading is you, or your rule, on the market as it happens, with the uncertainty intact. It closes the loop from idea to test to live behaviour without real money. Analess does not execute real trades or hold funds.

Trading journal

Analess trading journal listing seeded demonstration entries with symbol, side, date and status.

The record your future self can learn from.

  • Log, tag and review every decision, with the chart attached
  • Capture the reason you acted, not just the fill
  • Filter and review a history rather than scrolling a spreadsheet
What sets it apart

The journal lives in the same workspace as the chart the decision came from, so an entry links back to the exact chart, drawings and study you were looking at. Reviewing a decision means reopening it, not reconstructing it from memory.

Guide coming soon

Organizer

Analess organizer with To-Do, Diary and Rules views.

To-dos, a trading diary and your own rules, in one place.

  • To-do lists for what needs doing next
  • A diary for the running narrative alongside the journal
  • A place to write your own rules down and actually revisit them
What sets it apart

Written rules only help if you see them. The organizer sits in the same workspace as your charts and journal, so your rules are a tab away while you work, and the journal can record whether a decision followed them.

Guide coming soon

Analytics

Results are hypothetical and do not predict future performance

Illustration of the interface.

The numbers behind the record.

  • Distribution, expectancy and drawdown metrics over your history
  • The same metric definitions everywhere, so two surfaces never disagree

Past results, whether simulated or recorded, do not indicate future performance.

What sets it apart

When a backtest, a journal review and a portfolio summary each compute expectancy their own way, the numbers diverge and none of them can be trusted. Analess computes every metric in one shared engine, so the figure in your backtest and the figure in your journal mean the same thing.

Alerts

Alert delivery and platform availability remain subject to release verification.

Illustration of the interface.

Get told when a condition you defined becomes true.

  • Alert on conditions you build in the strategy builder
  • Mechanical and factual: an alert reports a state, it does not tell you what to do
What sets it apart

An alert is a graph like everything else, so it can watch anything you can build: an indicator crossing, a detected pattern, a model output or a condition written in code, not just a price level.

Intelligence

An agent that works the workspace with you, on the model you choose, and asks before every change.

AI agent

Analysis, never advice — it proposes, you approve

Concept diagram of the review step.

Describe the outcome. The agent works across the app to get there.

  • Opens charts, adds indicators, draws and annotates, builds strategy graphs and arranges layouts
  • Choose exactly what workspace context to share with the model
  • Every change is proposed for your approval, and one undo reverts it
  • It never places orders, and never touches your keys or security settings
What sets it apart

The agent can carry a task across tools using the workspace actions above. Review the proposed changes as a whole before applying them. Model responses may contain errors and are not endorsed by Analess; inspect their reasoning rather than treating them as verified conclusions.

Bring your own model

Illustration of the interface.

Use a supported model inside your research tools.

  • Connections depend on provider and platform support
  • Use the connection for workspace assistance and inline code suggestions
  • Choose the model through your own account
What sets it apart

The connection supplies the model; the AI agent and code-assistance tools define how you use it. Provider availability, account requirements and separate charges are explained in Integrations.

Model connections and costs →

Code assistance

Provider and platform availability vary; inspect generated code before use.

Illustration of the interface.

Inline suggestions while you write.

  • Completions and drafts in the editor, which you accept or ignore
  • Suggestion, never silent substitution: the code you run is the code you read
What sets it apart

Suggestions appear as ghost text you accept with one key, so you stay the author. They work in Python and JavaScript, including inside a code node in a strategy graph.

Foundation

The parts under everything else: your data sources, your devices and your workspace.

Markets & data

Illustration of the interface.

The same research tools across supported markets.

  • Choose a feed for its supported instruments and history
  • Crypto, forex and equities share charting and indicator calculations
  • One symbol search and one chart across every market
What sets it apart

Move from a supported symbol to its chart without changing the way you build studies. Instruments, history and fundamentals depend on the feed. See Integrations for the provider catalogue and connection requirements.

Data sources and requirements →

Workspaces & devices

Release availability and browser compatibility are still being verified

Analess workspace with a second chart floating in a panel above the main chart.

A workspace you arrange once, on every device you use.

  • Layouts adapt across desktop, browser and mobile
  • A docking workspace: split, tab, stack and float panels the way you work
  • Several named workspaces, with saved panel arrangements
  • On Windows, move a tab into its own desktop window for a second monitor
  • Available in 15 languages, including all its educational materials
  • Accessibility built in: screen-reader labels, a spoken chart summary and reduced-motion support
What sets it apart

Drag a tab to an edge to split a pane, or drop it in the middle to join it. Every arrangement also has a command, because dragging is not available to everyone. Saved layouts organise work on this device; moving research between devices is the separate cloud-sync capability below.

Platforms and requirements →

Cloud sync

Your storage, not ours. Described as designed — it is not operational yet

Illustration of the interface.

Pick up on any device, exactly where you left off.

  • Your workspaces, layouts, drawings and strategies, on every device you use
  • A personal-cloud connection, configured and authorised by you
  • Content encrypted on your device before upload

Cloud sync requires a configured storage provider and your authorisation. Encryption of the content does not hide file counts, sizes or upload activity from that provider.

What sets it apart

Sync is intended to carry a research session from one supported device to another. That is separate from adapting the layout to a smaller screen. Security explains recovery-phrase loss, metadata visibility and the limits of the encrypted copy.

Sync protection and recovery →

Your data

At-rest encryption defaults on for desktop and mobile with an OS-keystore key. The browser database is encrypted, but its default key is unprotected until Browser protection is enabled.

Illustration of the interface.

Local by default, encrypted by default.

  • Watchlists, drawings, strategies, journal and settings stored on your device
  • Encrypted at rest by default
  • Cached chart history can be studied offline; new data needs a connection

Browser protection is opt-in; the default browser key is unprotected. Connected services have separate data paths.

What sets it apart

Local-first describes where the research is kept, rather than promising every app interaction works offline. Security explains account services, diagnostics, AI context and platform-specific key protection.

Data paths and encryption →

Learning

Illustration of the interface.

Help at the point you need it.

  • Open an indicator’s explanation from the picker
  • Drawing hints describe the next placement step
  • Guided tutorials point to the controls you are using
What sets it apart

Look up a calculation, try the tool and return to your research without rebuilding the context. Learn brings together the wider ecosystem: reference material, lessons and planned social learning, with development captures of contextual help.

Explore the learning ecosystem →

Explore at your own pace.

See what is available today and what comes next on the roadmap, then choose your next step.

Get startedSee the roadmap