Backtest vs. Live Reconciliation
Tag trades by strategy name or version, then compare live fill prices, slippage, and P&L against your backtested expectations to identify execution gaps.
Algorithmic trading relies on systematic execution, precise backtesting, and rigorous performance measurement. Whether you're running quantitative strategies, testing machine learning models, or fine-tuning parameter sets, a structured trading journal helps you bridge the gap between simulation and live markets. Mantis gives algorithmic traders a private, on-device space to log live executions, compare results against backtests, and isolate implementation issues — from slippage and latency to unexpected market conditions. Because your data stays entirely on your iPhone or iPad with no cloud sync, you control your intellectual property while building a feedback loop that sharpens strategy development and risk management over time.
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Why traders use Mantis
Parameter & Version Tracking
Record which parameter set, model version, or configuration was active for each trade, making it easier to isolate performance differences across iterations.
Execution Quality Metrics
Log actual fill times, spreads, and market conditions to measure how latency, liquidity, or broker routing affects your systematic edge.
Offline, On-Device Privacy
All trade data, strategy notes, and performance stats live exclusively on your device — no cloud upload, no third-party access to your proprietary signals or results.
Visual Performance Analytics
Review equity curves, drawdown periods, and win-rate trends by strategy or time window to spot drift or regime changes that backtests may have missed.
Habit-Driven Workflow
Build a post-execution routine: log every automated trade, note any manual overrides or halts, and document system behaviour for continuous improvement.
Frequently asked questions
Can I import trade history from my execution platform or broker API?
Mantis supports CSV import, so you can export fills from most brokers or trading platforms and bring them into your journal. Once imported, you can add notes, tags, and context around each execution.
How do I separate trades by algorithm or strategy version?
Use Mantis's tagging and custom fields to label each trade with a strategy name, version number, or parameter set. Filters and search let you isolate performance by tag for side-by-side comparison.
Is my algorithmic strategy data kept private?
Yes. Mantis stores all trades, notes, and analytics locally on your device only — there is no cloud sync, no server upload, and no external access to your proprietary strategies or results.
Can I track metrics like slippage or latency?
You can log custom fields for each trade — expected fill, actual fill, timestamp, spread — then review those metrics over time to quantify execution quality and infrastructure performance.
Does Mantis help identify when a live strategy diverges from backtest results?
By tagging trades with strategy identifiers and logging key execution details, you can compare live P&L, drawdowns, and win rates against your backtested benchmarks to spot implementation drift or regime shifts.
Important: Mantis is a trading journal and analytics tool. This page is not investment advice, not financial advice, and not a recommendation to buy or sell any security or instrument. Trading involves risk; past performance does not guarantee future results. Consult a qualified professional before making financial decisions.