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Feature overview

Algomaker lets traders design algorithmic trading strategies visually, backtest them on real market data, optimize their parameters, and deploy them to a live broker — no code required, with the option to drop into Python for full control.

Designing bots

Visual node editor. Users build trading strategies by dragging "parts" onto a canvas and wiring their outputs into other parts' inputs. A bot is a connected graph: a data source feeds indicators, indicators feed gates and combiners, and the result drives an Order part that decides when to buy and sell. The canvas supports panning, zooming, multi-select, and keyboard-driven part deletion, so larger bots stay navigable.

• Drag-and-drop part placement from a right-click part menu.
• Click-and-drag connections between part inputs and outputs.
• Per-part settings panel for tuning numerical parameters.
• Naming and renaming bots from the editor.
• Pick a primary data source (the instrument the bot trades on).
• Pan, zoom, and center-view shortcuts for navigating large bots.
• Multi-client warning if the same account opens the editor in two places.

Indicators and signal parts

A library of building blocks. Algomaker ships with a catalog of pre-built parts covering the common ingredients of a trading strategy — trend indicators, oscillators, gates, and a configurable order executor. Parts emit a normalized signal in the range -1 (sell) to +1 (buy), so they compose cleanly: an EMA crossover and an RSI oscillator can feed a consensus combiner without any glue code. The Order part is where the user turns signals into trades and sets risk parameters.

• Trend & momentum: EMA crossover, MACD, RSI, fast stochastic.
• Volume & flow: OBV gate.
• Volatility & spread: volatility gate, spread deviation gate.
• Trend filters: trend gate.
• Combiners: consensus (averages multiple signals).
• Testing utilities: Rando (random signal) and Determo (deterministic signal) for sanity-checking strategy plumbing.
• Instrument: the data-source part that pipes market candles into the rest of the bot.
• Order: configures commitment (position size), transaction cost percentage, max gain, max loss, and whether short selling is allowed.

Custom parts

Write your own indicator without leaving the editor. When the built-in library doesn't have what the user needs, they can author a custom part — either by describing what they want in plain language and letting Algomaker generate the code, or by writing Python directly in an in-app code editor. Custom parts get their own JSON-driven settings UI, the same as built-in parts, so they're indistinguishable from native ones once placed on the canvas.

• AI-assisted part generation from a natural-language prompt.
• In-app Python editor (Ace) with syntax highlighting and validation.
• JSON-defined settings schema that renders a native-feeling settings panel.
• Tabbed editing dialog (AI / Settings / Code) for iterating on a custom part.

Charts and signals

See what your bot sees. Every bot is paired with a candlestick chart of its primary instrument, rendered with a financial-grade charting engine. After a backtest the chart overlays buy and sell markers on the candles where the bot would have traded, with trade annotations that explain each entry and exit. The user can toggle between bid, offer, mean, and spread views, and a dedicated spread pane visualises the bid/offer gap separately.

• Candlestick chart with bid/offer/mean/spread views.
• Buy/sell markers showing where the bot traded.
• Trade annotations linking entries to their matching exits.
• Spread pane for visualizing bid/offer separation.
• Instrument info plate showing the active market's key metadata.
• Rollover tooltips and zoom/pan interaction.

Backtesting and analysis

Measure before you risk capital. As soon as a bot is wired up, Algomaker can run it across historical candle data and report how it would have performed — total return, win rate, drawdown, max loss, and other key metrics — in the Bot Info panel next to the chart. The event log and signal log let users drill into individual decisions to understand why the bot opened or closed a position.

• Bot Info panel with performance metrics (return, win rate, drawdown, max loss, etc.).
• Event log of every notable bot decision.
• Signal log for inspecting per-part signal values over time.
• Part state indicators showing each part's live output on the canvas.
• One-click sim run to re-evaluate the bot after a change.
• Reload from server to refresh historical data and re-run.

Optimization

Find the best parameters automatically. The Part Optimizer sweeps the numerical settings of any part across a user-defined range and reports which combination produced the best performance against the current bot and dataset. Instead of nudging an EMA period by hand and re-running, the user defines the search space and lets the optimizer surface the winner — and the runners-up — so they can pick the configuration that suits their risk preference, not just the top result.

• Define per-setting search ranges and step sizes.
• Optimizer surfaces ranked results, not just the single best.
• Apply the chosen configuration back into the bot with one click.

Live trading

Deploy a bot to a real broker. Once a backtest looks good, the user can deploy the bot to their IG broker account — demo or live — with a confirmation dialog that prevents accidental deployments. While deployed, the bot processes live market candles tick by tick on the server side, opens and closes positions through the broker, and reports trade outcomes back to the user. Withdrawing a bot stops it from trading without losing its configuration.

• Deploy / withdraw a bot to/from the connected broker.
• Demo and live IG accounts both supported.
• Server-side execution: live trading continues whether or not the editor is open.
• Deployment status indicator on each bot in the bot list.

Broker account

Log in to your broker, once. Users connect their IG account through Algomaker by entering their IG credentials and API key in the user profile. Credentials are verified against IG up front so login problems surface immediately, not at deploy time. After verification, Algomaker keeps the broker session alive in the background so the user doesn't need to re-authenticate between sessions.

• Log in to IG (demo or live) from the user profile.
• Test broker credentials before saving.
• Broker account info panel (account ID, balance, currency, account type).
• Automatic background session refresh — the user stays logged in.

Workspace and bots

Run more than one strategy. Users can keep a library of bots in their workspace, switch between them from the bot selector, and have each bot remember its own canvas layout, parts, and settings. Bots can be saved at any time, reloaded from the server, and (when bot sharing is enabled) shared with another user.

• Bot selector listing all of the user's bots.
• Quick switch between bots without losing canvas state.
• Save / load bots to the server.
• Bot sharing — share a bot configuration with another user.

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Coming soon

Features that are designed or in development but not yet available to all users. Items in this section may move into the main inventory as they ship.

• Slippage modelling — more realistic simulation of execution price drift in backtests.
• Order part tooltips — inline explanations of every Order setting directly in the settings panel.
• Max-loss display in Bot Info — the bot's configured max-loss surfaced alongside performance metrics.
• Suppress chart signals when max open positions reached — chart markers only render where the bot could actually have traded given its position cap.
• Aggregate gate part types — a unified family of gate parts that share a consistent settings interface.
• MFI (Money Flow Index) part — new built-in oscillator combining price and volume.
• Dynamic bot simulation — backtesting for bots whose parameters re-optimize over time.
• Dynamic bot live re-optimization — live bots that periodically re-fit their own parameters as the market changes.