Macro Trade ingests order flow and price data from currency, crypto, equity, and commodity venues, converting it into ranked, risk-weighted signals. Built for traders who evaluate probability, not sentiment.
Macro Trade was built to give professional and semi-professional traders access to the type of quantitative infrastructure historically reserved for institutional desks. The platform combines statistical modeling with a transparent methodology, so every signal can be traced back to its underlying data inputs.
Rather than issuing buy-or-sell calls, Macro Trade presents probability-ranked signals alongside the data that produced them. Traders remain responsible for position decisions; the system's role is to reduce the time spent on manual screening across 500+ pairs.
Every signal passes through five distinct stages before reaching a trader's dashboard. Each stage is logged, so the origin of a given signal can be reviewed after the fact.
Streaming and batch feeds from spot, futures, and order-book sources are collected and time-stamped for synchronization.
Raw inputs are converted into standardized variables: volatility bands, momentum shifts, liquidity depth, and cross-asset correlation.
Trained statistical models assign a directional probability and confidence score to each monitored pair.
Outputs below the confidence threshold set by the risk engine are discarded before distribution.
Approved signals are pushed to the dashboard and connected endpoints via WebSocket or REST, with a logged latency measurement.
Methodology note: Models are retrained on rolling historical windows and validated through out-of-sample backtesting before deployment. No signal is distributed without passing the minimum confidence threshold defined by the risk-weighting engine described in the next section.
Macro Trade monitors 500+ trading pairs across four primary asset classes. Refresh intervals differ by venue liquidity and asset type, and are listed below per class.
| Asset class | Pairs covered | Update interval | Signal latency (target) |
|---|---|---|---|
| Foreign exchange | 150+ | 250ms | <500ms |
| Crypto assets | 200+ | 100ms | <300ms |
| Equities | 100+ | 1s | <1.2s |
| Commodities | 50+ | 500ms | <800ms |
Signals are not distributed in isolation. Each one passes through a risk-weighting layer designed to account for volatility, correlation, and position exposure before it reaches the dashboard.
Inputs are standardized across venues to remove formatting and timing discrepancies.
Each pair receives a probability-weighted ranking based on model confidence.
Volatility and correlation exposure are applied to adjust the raw score.
Suggested allocation ranges are shown, not fixed trade amounts.
Open signals are re-evaluated as new data enters the pipeline.
Position sizing guidance is derived from volatility-adjusted models that account for drawdown constraints and correlation between concurrently open positions. The goal is to flag concentration risk before it becomes visible in account equity, rather than after.
Guidance ranges are recalculated whenever a new signal enters an existing exposure cluster, so a trader holding correlated positions sees an updated risk read, not a static number.
Macro Trade is designed to sit alongside tools traders already use, rather than replace an existing execution stack.
Each tier scales by number of monitored pairs, API call volume, and support level. Exact pricing depends on usage and is confirmed during onboarding.
Common technical questions from traders evaluating the platform before connecting an account or API key.
Data is aggregated from regulated exchanges and liquidity providers across the covered asset classes. Feeds are time-stamped on receipt and normalized before entering the modeling pipeline, so timing differences between sources do not distort signal output.
Target latency ranges from under 300ms for crypto pairs to under 1.2 seconds for equities, depending on venue update frequency and network conditions between the data source and the Macro Trade pipeline. These are design targets, not guaranteed figures for every market condition.
No. Macro Trade is a data-analysis and decision-support tool. It does not place trades or manage funds, and outputs should not be treated as personalized investment advice. Traders remain responsible for their own decisions and compliance obligations.
Data is encrypted in transit and at rest, with role-based access controls on account-level information. Infrastructure choices are made with GDPR requirements in mind for users in Germany and the wider EU.
Yes. Every distributed signal is logged with its input features and confidence score, so it can be reviewed after the fact through the dashboard or API audit endpoint.
Transparency statement: Macro Trade does not claim guaranteed returns. Model outputs reflect historical pattern analysis and probability estimation; markets can and do behave outside the range of prior data.
Create an account to review live dashboard output, check latency against your own venue connections, and inspect the methodology behind a sample of recent signals.
Data is encrypted in transit and at rest. Infrastructure and data handling practices are designed with GDPR compliance in mind for users in Germany and the EU.