Terminal modules

Thirteen modules for the full trading loop.

Each module in Dome Terminal is designed to answer a specific trading pressure. Together they create a connected workflow where every decision has more context.

Trading is not only about finding a clean entry. It is about preparing the session, understanding the regime, sizing the risk, documenting the decision, and reviewing the outcome without rewriting history after the fact.

Dome Terminal is built around that full loop. The product brings charts, orderflow, scripting, bots, portfolio risk, market intelligence, journaling, alerts, Academy lessons, and AI review into a single professional desktop workflow.

The platform is deliberately opinionated about truthfulness. When data is unavailable, the terminal should say so. When an AI suggests a change, the trader should confirm it. When a backtest looks strong, robustness, walk-forward behavior, and risk metrics still matter.

That philosophy gives serious traders a calmer way to work. Instead of chasing scattered dashboards and generic chatbot answers, the trader can ask better questions from inside the exact context where the decision is being made.

  • Use chart, journal, portfolio, bot, and strategy context together instead of treating each workflow as a separate island.
  • Review trades with real execution history so the same mistakes become measurable instead of remaining vague memories.
  • Build and test strategies with a scripting workflow designed for trading logic, invalidation, risk, and repeatable conditions.
  • Use AI for analysis, explanation, mentoring, and proposal drafting while keeping the trader in control of every meaningful action.
  • Track market regimes, liquidity zones, volatility, and relative strength so setups are judged inside the environment that produced them.
  • Protect the account with daily loss awareness, drawdown context, bot gates, alerting, and portfolio-level exposure review.
  • Learn from Academy content that can connect lessons to chart behavior, journal patterns, and the setups a trader actually takes.
  • Keep the platform honest with clear unavailable states when a feed, provider, sync source, or live account connection is not ready.
OR

ORACLE AI Trading Copilot connects directly to the way active traders prepare, measure, automate, and review decisions.

ORACLE AI Trading Copilot

ORACLE AI Trading Copilot is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of ORACLE AI Trading Copilot is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, ORACLE AI Trading Copilot gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • ORACLE reads the active chart, timeframe, watchlist context, recent trades, portfolio state, and journal patterns before it answers.
  • Every trading proposal is separated from execution, so the trader can inspect, reject, or approve the next step with intent.
  • The assistant can explain a Dome Script, review a backtest, summarize risk, and turn a chart question into a practical checklist.
  • Streaming conversations keep the workflow fast during active sessions when a trader needs context without leaving the desk.
  • Persistent conversation history keeps research threads alive across strategy reviews, journal sessions, and market preparation.
  • Markdown, tables, structured proposals, and report-style answers make the output useful enough to keep as research notes.
  • Local and cloud model choices let traders balance speed, privacy, cost, and reasoning depth for different tasks.
  • Action audit trails help serious traders review what the AI suggested, what was accepted, and what was ignored.
CO

AI Committee connects directly to the way active traders prepare, measure, automate, and review decisions.

AI Committee

AI Committee is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of AI Committee is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, AI Committee gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • ATLAS studies price action, market structure, trend quality, chart patterns, and indicator context before giving a technical verdict.
  • FLOW focuses on orderflow, footprint behavior, cumulative delta, absorption, stacked imbalances, and depth pressure.
  • PULSE reads sentiment, news flow, social pressure, and the emotional temperature around the asset being reviewed.
  • SHIELD checks position sizing, daily loss pressure, drawdown exposure, risk-to-reward quality, and account survival.
  • CHAIN reviews on-chain flows, exchange movements, whale behavior, and network health where reliable data is available.
  • MACRO considers economic events, correlations, market cycles, liquidity windows, and broader risk-on or risk-off conditions.
  • QUANT examines statistical edge, backtest quality, regime behavior, robustness, and whether the trade survives measurement.
  • SNIPER focuses on timing, invalidation, entry location, target quality, and whether patience would improve the execution.
TR

Trade Desk connects directly to the way active traders prepare, measure, automate, and review decisions.

Trade Desk

Trade Desk is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Trade Desk is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Trade Desk gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.
OR

Orderflow connects directly to the way active traders prepare, measure, automate, and review decisions.

Orderflow

Orderflow is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Orderflow is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Orderflow gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.
DO

Dome Script Lab connects directly to the way active traders prepare, measure, automate, and review decisions.

Dome Script Lab

Dome Script Lab is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Dome Script Lab is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Dome Script Lab gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.
BO

Bot Management connects directly to the way active traders prepare, measure, automate, and review decisions.

Bot Management

Bot Management is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Bot Management is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Bot Management gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.
PO

Portfolio Hub connects directly to the way active traders prepare, measure, automate, and review decisions.

Portfolio Hub

Portfolio Hub is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Portfolio Hub is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Portfolio Hub gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.
QU

Quant Lab connects directly to the way active traders prepare, measure, automate, and review decisions.

Quant Lab

Quant Lab is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Quant Lab is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Quant Lab gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.
QU

Quant Brain Pro connects directly to the way active traders prepare, measure, automate, and review decisions.

Quant Brain Pro

Quant Brain Pro is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Quant Brain Pro is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Quant Brain Pro gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.
MA

Market Intel connects directly to the way active traders prepare, measure, automate, and review decisions.

Market Intel

Market Intel is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Market Intel is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Market Intel gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.
JO

Trade Journal connects directly to the way active traders prepare, measure, automate, and review decisions.

Trade Journal

Trade Journal is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Trade Journal is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Trade Journal gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.
AC

Academy connects directly to the way active traders prepare, measure, automate, and review decisions.

Academy

Academy is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Academy is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Academy gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.
AL

Smart Alerts connects directly to the way active traders prepare, measure, automate, and review decisions.

Smart Alerts

Smart Alerts is written for traders who already know that more indicators do not automatically create edge. The goal is to pull market context, risk, execution history, and review into one disciplined workflow so each decision is easier to defend after the candle closes.

The strongest part of Smart Alerts is that it treats trading as a process instead of a single prediction. A trader can prepare, test, execute, review, and refine from the same environment, which reduces the drift that happens when notes, charts, scripts, and risk checks live in separate places.

For discretionary traders, Smart Alerts gives structure without removing judgment. For systematic traders, it gives measurement without hiding the assumptions behind clean equity curves. The product language is intentionally grounded in invalidation, liquidity, regime, drawdown, expectancy, and repeatable behavior.

The end result is not a promise that every trade becomes easy. It is a workspace where mistakes become visible, strong setups become easier to repeat, and weak ideas are forced through enough context that the trader has a better chance of standing aside.

  • The workflow is designed for traders who need fewer disconnected tools and more decisions grounded in the same market context.
  • The interface keeps the trader close to price, risk, execution history, and research instead of forcing constant context switching.
  • Every major surface is built around real market state, cached history, or an honest unavailable state when a provider is not ready.
  • The platform emphasizes reviewable evidence: chart levels, backtests, journal outcomes, bot logs, alerts, and quantified regimes.
  • AI features are positioned as decision support, not magic signals, with confirmation steps around actions that can change data.
  • Local-first workflows help traders continue research, review, and cached market analysis even when a live provider is unavailable.
  • Risk controls appear throughout the product because a strong entry means little when sizing and drawdown rules are ignored.
  • The product connects education, execution, automation, and review so the trader improves the process instead of only chasing trades.