GITNUXSOFTWARE ADVICE
EconomicsTop 10 Best Ea Backtesting Software of 2026
Compare rankings of ea backtesting software tools for algorithmic testing, with picks like QuantConnect, TradingView Strategy Tester, Forex Strategy Builder.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Forex Strategy Builder is the best fit for MetaTrader EA backtesting when you want rule-based Forex execution behavior and repeatable parameter sweeps, while Wealth-Lab suits developers who need tight edit-test-report loops for EA logic under consistent assumptions and Forex Tester works best if you need repeatable EA backtests with execution-cost modeling inside the MetaTrader testing workflow.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Forex Strategy Builder
Execution engine with explicit order handling that preserves EA position lifecycle through historical runs.
Built for fits when MetaTrader EA backtesting needs consistent Forex execution behavior and repeatable parameter sweeps..
Wealth-Lab
Editor pickIntegrated strategy scripting and report generation in one workflow for rapid backtest iterations.
Built for fits when strategy developers need tight edit-test-report loops for EA logic under consistent assumptions..
QuantRocket
Editor pickBroker-aware historical data configuration paired with queued backtest runs and structured report exports.
Built for fits when teams run many EA backtests and need consistent, exportable reporting across brokers and symbols..
Related reading
Comparison Table
EA backtesting software tools determine how reliably an automated strategy can be reproduced from historical data, then stress-tested under consistent settings and execution assumptions. This ranked list targets analysts and operators comparing backtest automation, data-modeling and API access, and audit-ready experiment records, with picks like QuantConnect used as a benchmark for scalable research workflows.
Forex Strategy Builder
vertical specialistForex strategy design and backtesting software with rule-based construction and analysis.
Execution engine with explicit order handling that preserves EA position lifecycle through historical runs.
Forex Strategy Builder runs automated trading strategy testing workflows that emphasize trade execution sequences and position lifecycle events rather than only bar-level signals. The backtests produce equity curve and performance breakdown outputs that map to EA-level outcomes like profit factor, drawdown, and trade distribution. Historical simulation coverage targets Forex inputs including bid-ask spread and order execution effects so results reflect more than direction-only behavior.
A tradeoff appears in workflow breadth, since integrations and data source variety are narrower than cloud research platforms such as QuantConnect. The tool fits situations where a MetaTrader EA already exists, and where repeatable parameter sweeps and walk-forward style comparisons are needed on Forex pairs with consistent execution assumptions.
- +MetaTrader EA focused testing workflow with execution-oriented results
- +Trade-by-trade reporting supports equity curve and drawdown analysis
- +Spread handling improves realism versus simple mid-price modeling
- +Batch runs make parameter sweeps practical for repeated comparisons
- –Execution realism depends on the quality and fit of input data
- –Advanced custom research pipelines require more manual export handling
- –Integration depth beyond MetaTrader-style testing is limited
- –Modeling granularity can be constrained versus tick-level research stacks
MetaTrader EA traders
Validate EA parameter sets on Forex pairs
Faster parameter comparison cycles
Quant strategy analysts
Audit execution assumptions in EA results
Clearer execution-driven divergences
Show 2 more scenarios
Trading teams
Produce consistent walk-forward comparisons
More consistent robustness checks
Generate out-of-sample style segment results using repeatable test configurations.
Algorithm developers
Debug EA behavior against history
Faster EA bug isolation
Use event-level reporting to identify when order logic diverges from expectations.
Best for: Fits when MetaTrader EA backtesting needs consistent Forex execution behavior and repeatable parameter sweeps.
More related reading
Wealth-Lab
SMBStrategy research platform for coding, backtesting, screening, and portfolio analysis.
Integrated strategy scripting and report generation in one workflow for rapid backtest iterations.
Wealth-Lab supports automated trading strategy testing through a strategy editor that connects directly to backtest runs and generates detailed trade and performance summaries. Execution modeling focuses on how orders behave during historical runs, and report outputs include equity curve style metrics and trade-level breakdowns that support parameter iteration. For EA authors who already think in terms of repeatable experiments, Wealth-Lab provides a workflow for running a sequence of tests and comparing results without building separate tooling.
A key tradeoff is that Wealth-Lab’s realism depends on the available historical inputs and the execution assumptions selected for the run. It fits best when strategy logic can be expressed in the Wealth-Lab scripting model and when the historical data quality matches the markets being tested. Usage is strongest for repeatable in-sample testing cycles and for refining entry and exit rules using report feedback.
- +Strategy scripting connects directly to repeatable backtest runs
- +Trade-level reporting supports faster diagnosis than summary-only outputs
- +Execution assumptions are configurable per test run
- +Consistent report outputs help compare parameter variants
- –Realism is limited by the historical data inputs available
- –Advanced optimization workflows require careful test setup discipline
Quant-minded retail traders
Iterate EA parameters with detailed reports
Faster parameter refinement
Systematic strategy developers
Validate entry and exit rules
Cleaner strategy selection
Show 2 more scenarios
MetaTrader-focused EA authors
Test EA logic before platform deployment
Reduced deployment surprises
Stress-test strategy behavior on historical runs using configurable execution assumptions and diagnostics.
Small research teams
Batch experiments across market conditions
More disciplined experimentation
Run multiple strategy and assumption sets and use consistent outputs to narrow promising configurations.
Best for: Fits when strategy developers need tight edit-test-report loops for EA logic under consistent assumptions.
QuantRocket
API-firstDocker-based quantitative trading platform with data management, research, and backtesting tools.
Broker-aware historical data configuration paired with queued backtest runs and structured report exports.
QuantRocket manages historical price ingestion and normalizes results across repeated runs so parameter scans and out-of-sample testing stay consistent. Backtests are executed as queued jobs with configurable run settings, and results are organized for reporting and downstream analysis. The automation surface is strongest when a research workflow needs many iterations across strategies, symbols, and parameter sets.
A key tradeoff is that tick- and spread-fidelity depends on the quality and availability of the selected broker datasets. QuantRocket fits best when automated EA testing is run at scale and results must be exported and compared, rather than when one-off manual testing is the main workflow.
- +Repeatable run configuration for batch EA backtesting and comparisons
- +Broker-aware price ingestion that reduces dataset mismatch risk
- +Report exports that support custom analysis pipelines
- +Automation hooks for scheduled or multi-run research batches
- –High-fidelity modeling depends on the chosen broker data quality
- –Complex workflows can require more upfront run configuration discipline
- –Some execution detail modeling may lag what full custom engines provide
- –Workflow complexity rises with large cross-asset parameter sweeps
Quant research teams
Batch-compare EA parameters across symbols
Faster sensitivity screening
MetaTrader EA analysts
Run broker-specific history for consistency
More comparable trials
Show 2 more scenarios
Trading ops automation teams
Schedule repeatable backtest jobs
Lower manual workload
Automate repeated runs and exports for ongoing research and verification cycles.
Portfolio strategists
Export metrics for equity curve analysis
Clearer risk diagnostics
Pull standardized results for drawdown review and trade distribution comparisons.
Best for: Fits when teams run many EA backtests and need consistent, exportable reporting across brokers and symbols.
cTrader Algo
vertical specialistTrading platform with C# algorithm development, backtesting, and parameter optimization.
cBot-first testing that mirrors cTrader’s execution semantics inside the built-in Strategy Tester.
cTrader Algo targets expert advisor backtesting workflows inside the cTrader ecosystem, with a Strategy Tester that runs cBots against historical market data. It integrates tightly with cTrader’s order execution and strategy deployment model, which reduces mismatch between how logic is executed and how signals are tested.
Backtests support parameter sweeps and report outputs designed for comparing strategy variants across time windows. The differentiator is how closely Algo tooling stays aligned with cTrader’s cBot programming and trade simulation pipeline.
- +Tight alignment between cBot execution flow and strategy tester results
- +Parameter optimization runs can compare multiple strategy variants efficiently
- +Report outputs make it easier to review equity curve and trade outcomes
- +Uses cTrader’s trading account model to keep simulation intent consistent
- –Tick-level replay depth is limited compared with dedicated tick-data toolchains
- –Advanced execution effects like detailed latency modeling are not first-class
- –Cross-broker price fidelity testing needs careful data source selection
- –Complex walk-forward setups require manual workflow orchestration
Best for: Fits when teams already standardize on cTrader and want fast EA testing inside the same execution model.
QuantConnect
API-firstCloud and local algorithmic trading platform with historical data and backtesting infrastructure.
Cloud-hosted backtesting with the same algorithm code used for live execution, reducing research-to-trade drift.
QuantConnect runs automated trading strategy testing through its cloud backtesting and live trading workflows, using a code-driven research environment. The engine supports both bar-based and tick-by-tick modeling so EA backtests can approximate intrabar behavior with broker-style execution inputs.
Strategy parameters plug into the research pipeline for repeatable runs across assets and time windows, with reporting exports for performance review. Integration with external services via API and job automation supports end-to-end testing-to-deployment cycles.
- +Tick-by-tick modeling supports more realistic execution paths than bar-only tests
- +Cloud backtests scale across many symbols and dates without local resource limits
- +Research-to-live workflow keeps code and configuration aligned across environments
- +Automation hooks support repeated runs for optimization and regression testing
- –Tick modeling quality depends on historical tick data availability and fidelity
- –Execution modeling still needs careful parameterization for slippage, commission, and spread assumptions
- –EA workflows with heavy custom data sources require engineering work
- –Cross-market out-of-sample and walk-forward setups demand deliberate partitioning logic
Best for: Fits when algorithmic teams need code-first EA testing with repeatable automation and execution modeling.
MultiCharts
SMBTrading platform with automated strategy development, portfolio backtesting, and optimization.
A workflow that links strategy projects, backtest runs, and trade reporting inside one execution-focused environment.
MultiCharts is EA backtesting software aimed at traders who need an execution-aware workflow tied to strategy code. It supports automated trading strategy testing using multi-instrument strategy projects and historical market data playback.
The workflow centers on designing strategies in a dedicated development environment, running backtests, and reviewing performance and trade statistics. MultiCharts is most distinct for how it keeps strategy logic, backtesting runs, and reporting tightly coupled inside one toolchain.
- +Strategy development and backtest execution stay in one environment
- +Batch backtesting supports comparing many parameter sets across runs
- +Detailed trade-level reports support equity curve and drawdown review
- +Multi-instrument projects support realistic portfolio-style testing
- –Advanced execution modeling requires careful event timing and settings
- –Automation via external APIs is less direct than code-forward platforms
- –Project setup overhead can slow iteration for small experiments
- –Report export formats are narrower than spreadsheet-first toolchains
Best for: Fits when strategy coders need repeatable backtest runs with trade-level reporting.
NinjaTrader Strategy Analyzer
SMBFutures and trading platform with automated strategy development and historical analysis.
Strategy Analyzer’s report and analysis views stay directly linked to NinjaTrader strategy runs.
NinjaTrader Strategy Analyzer targets NinjaTrader-native workflows for automated trading strategy testing, with a backtesting and analysis loop focused on strategy performance reports. It supports detailed trade and execution statistics tied to NinjaTrader strategy builds, which helps validate assumptions before forward testing.
The tool’s strength is integration depth with NinjaTrader’s strategy engine and reporting surfaces rather than standalone EA simulation. Results review includes drawdown, trade metrics, and exportable reporting for optimization and diagnostics.
- +Tight integration with NinjaTrader strategy engine and reporting
- +Execution and trade metrics are presented in a strategy-focused workflow
- +Built for parameter iteration using NinjaTrader strategy settings
- +Exportable report outputs for review and comparison
- –Tick-level modeling fidelity depends on the available NinjaTrader data feeds
- –Automation and external API access are limited compared with EA backtesting ecosystems
- –Cross-platform broker-specific simulation is constrained to NinjaTrader-compatible environments
- –Advanced execution modeling needs careful manual configuration
Best for: Fits when NinjaTrader users need disciplined strategy backtesting and repeatable performance diagnostics within one engine.
Forex Tester
vertical specialistForex simulation software for historical testing, manual replay, and automated strategy evaluation.
Configurable execution-cost simulation for spreads, commissions, and slippage directly within the tester run settings.
Forex Tester focuses on expert advisor backtesting for MetaTrader workflows with a built-in tester that runs strategies against historical broker data. The workflow supports configuration of trade execution assumptions like spreads, commissions, and slippage so results reflect execution friction rather than ideal fills.
Reports aggregate common performance signals such as equity curve behavior and drawdown, which helps compare parameter sets across runs. Automation remains centered on preparing test projects and batch-running scenarios from the same testing environment.
- +Execution modeling includes spread, commission, and slippage inputs
- +Backtest reporting highlights equity curve and drawdown outcomes
- +Supports consistent multi-run comparisons through saved test configurations
- +Works natively with MetaTrader expert advisors and strategy code
- –Automation is limited to project setup and batch runs rather than full API control
- –Tick modeling depth can be constrained by available historical tick quality
- –Walk-forward and Monte Carlo workflows depend on manual scenario orchestration
- –Large parameter grids increase runtime and require careful execution planning
Best for: Fits when teams need repeatable EA backtesting with execution-cost modeling inside a MetaTrader testing workflow.
StrategyQuant
vertical specialistAutomated strategy research software for generating, testing, and validating trading systems.
Batch backtest orchestration that runs the same EA configuration across parameter sets and generates comparison-ready results.
StrategyQuant is focused on automated backtesting and analysis for algorithmic trading strategies, including workflows that start from strategy idea to repeatable test runs. The tool emphasizes rigorous EA testing with support for historical data ingestion and strategy configuration runs across parameter sets.
Reporting for performance and trade metrics is designed to support parameter sensitivity work and iterative refinement cycles. It is also used alongside external strategy environments where execution reports need to be gathered and compared across trials.
- +Strong automation for batch strategy runs across parameter ranges
- +Detailed performance and trade analytics for comparing trial outcomes
- +Repeatable testing workflow supports in-sample and out-of-sample comparisons
- +Supports data-driven backtests that align with common EA evaluation needs
- –Workflow complexity increases when many parameters and models are combined
- –Tick-level modeling fidelity depends on the imported market data quality
- –Integration depth with broker-specific execution models can require extra effort
- –Guardrails for experiment governance are less explicit than in some competitors
Best for: Fits when EA developers need automated trial runs, parameter sensitivity reporting, and repeatable evaluation cycles.
AmiBroker
SMBDesktop technical analysis platform with AFL scripting, portfolio testing, and optimization.
AmiBroker Formula Language drives both signal generation and backtest analysis inside one workflow.
AmiBroker is a desktop-focused backtesting and research tool built around a scriptable formula language for strategy logic and performance reporting. It distinguishes itself with a built-in backtest engine, charting, and a large ecosystem of community indicators and data import workflows.
The platform supports parameter sweeps and repeated runs over historical datasets, and it exports results for deeper review of equity curves and trade statistics. For EA-style automation, AmiBroker is best when trading logic can be expressed in its AFL workflow and when data can be imported into its backtesting-compatible format.
- +AFL strategy scripting supports repeatable parameter optimization loops
- +Rich performance reports include trade list metrics and equity curve statistics
- +Tight integration between charts, signals, and backtest results
- +Extensive ecosystem of indicators and data import tooling
- –Tick-by-tick modeling and execution simulation depth are limited versus EA-centric testers
- –Automation and external API access are not the primary workflow
- –Broker-specific execution modeling needs custom data and script work
- –AFL has a learning curve for production-grade testing pipelines
Best for: Fits when EA logic is expressed in AFL and results need strong chart and report iteration on imported historical data.
Conclusion
After evaluating 10 economics, Forex Strategy Builder stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right ea backtesting software
EA backtesting software is judged by how faithfully it reproduces execution outcomes across historical runs and how reliably it keeps strategy state consistent as parameters change. This guide covers Forex Strategy Builder, QuantConnect, TradingView Strategy Tester, and other widely used tools like Wealth-Lab and QuantRocket to map the differences that show up in trade and equity results.
The top selection hinges on execution modeling controls and automation depth, since spread, commission, and slippage inputs shape the equity curve as much as the signal logic. The sections that follow focus on each tool’s run orchestration, reporting granularity, and workflow fit for MetaTrader EA testing, broker-aware runs, and cloud or local execution paths.
EA Backtesting Software That Reproduces Execution and Automates Parameter Runs
EA backtesting software simulates expert advisor behavior over historical data using an execution model that can include spread, commission, and slippage inputs. Forex Strategy Builder emphasizes an execution engine with explicit order handling that preserves EA position lifecycle through historical runs, which directly affects trade-by-trade equity curve and drawdown calculations.
QuantConnect focuses on code-first backtesting where cloud runs use the same algorithm code for live execution, and tick-by-tick modeling supports more realistic execution paths than bar-only tests. Wealth-Lab targets fast edit-test-report loops with integrated strategy scripting and trade-level reporting, which helps diagnose EA logic changes while keeping assumptions consistent across repeatable backtest runs.
EA backtesting evaluation features that change trades, execution, and automation outcomes
Execution modeling inputs determine whether backtest fills match real outcomes, because spread, commission, and slippage change the equity curve and drawdown path even when signals stay identical. Run orchestration matters just as much because parameter sweeps only stay comparable when order handling and position state stay consistent across historical runs.
Execution engine that preserves EA position lifecycle through historical runs
Forex Strategy Builder is built around an execution engine with explicit order handling that preserves EA position lifecycle through historical runs. This design changes trade-level equity curve and drawdown calculations when orders partially fill or close under historical conditions.
Tick-by-tick modeling depth and execution path realism
QuantConnect supports tick-by-tick modeling that produces more realistic execution paths than bar-only tests. The realism then depends on historical tick data availability and fidelity chosen for the run.
Broker-aware historical data configuration and batch backtest exports
QuantRocket pairs broker-aware historical data configuration with queued backtest runs and structured report exports. This combination reduces dataset mismatch risk when running many EA backtests across brokers and symbols.
Strategy scripting with tight edit-test-report loops and trade diagnostics
Wealth-Lab integrates strategy scripting and report generation so the same workflow runs the next backtest iteration. Trade-level reporting supports faster diagnosis than summary-only outputs when EA logic changes.
Execution-model alignment to the native cTrader workflow
cTrader Algo centers on cBot-first testing inside cTrader Strategy Tester so results match cTrader execution semantics. Parameter optimization runs compare multiple strategy variants efficiently within the same execution model.
Execution-cost simulation for spread, commission, and slippage inside run settings
Forex Tester includes configurable execution-cost simulation for spreads, commissions, and slippage directly in tester run settings. Backtest reporting then highlights equity curve and drawdown outcomes tied to those execution inputs.
How to choose EA backtesting software based on workflow philosophy and control depth
The best fit depends on how the backtester keeps strategy state consistent while execution assumptions change. Two tools can both report equity curves while using different order handling or execution timing, which can make outcomes diverge even under identical parameter values.
Pick explicit order lifecycle handling when EA state consistency across runs is the priority
Choose Forex Strategy Builder when the EA workflow depends on preserving order and position lifecycle through historical runs. This focus keeps trade-by-trade reporting aligned with equity curve and drawdown analysis when parameter sweeps change trade frequency.
Choose cloud code re-use with tick-by-tick modeling for research-to-trade drift control
Choose QuantConnect when the goal is using the same algorithm code for live execution while running cloud backtests at scale. Validate that the chosen tick data for each symbol and date set supports the slippage, commission, and spread assumptions needed for your execution model.
Choose broker-aware data ingestion plus queued runs when teams need repeatable cross-broker comparisons
Choose QuantRocket when broker-aware historical data configuration and queued backtest runs matter for consistent comparisons. Use its structured report exports to standardize how trade outcomes feed equity curve and performance comparisons across many runs.
Choose integrated scripting and trade-level diagnostics for rapid iteration on EA logic
Choose Wealth-Lab when the workflow needs a tight edit-test-report loop that keeps assumptions consistent between iterations. Use its trade-level reporting to isolate logic bugs faster than summary-only outputs.
Choose native execution semantics alignment when the execution model must match a specific platform
Choose cTrader Algo when testing cBot behavior inside cTrader Strategy Tester is required to mirror cTrader execution semantics. Confirm that tick-level replay depth meets needs because tick replay depth is limited compared with dedicated tick-data toolchains.
Who benefits from these EA backtesting software capabilities
Different backtesting stacks support different development workflows. The right choice follows from whether the EA logic is validated through execution realism, through broker-aware reproducibility, or through rapid scripting iteration.
MetaTrader EA users who need execution-focused historical order handling
Forex Strategy Builder fits when MetaTrader EA testing must preserve order and position lifecycle through historical runs. The resulting trade-by-trade reporting supports equity curve and drawdown analysis tied to execution behavior.
Algorithmic teams running many backtests across dates and symbols with automation
QuantConnect fits when cloud backtests must scale across many symbols and dates while reusing the same algorithm code for live execution. QuantRocket also fits when queued runs and structured exports are required for cross-broker consistency.
Strategy developers who iterate on EA logic and need tight edit-test-report loops
Wealth-Lab fits when the workflow combines strategy scripting with report generation to speed backtest iterations. Trade-level reporting helps diagnose EA logic changes faster than summary-only outputs.
Teams standardized on cTrader who need execution semantics parity in the tester
cTrader Algo fits when cBot execution flow must match cTrader Strategy Tester results. Parameter optimization runs support comparing multiple strategy variants inside the same execution model.
Common EA backtesting mistakes that mislead execution results
Backtests fail when execution assumptions change without controlled inputs or when market data quality silently shifts. These errors show up as equity curve differences that reflect modeling artifacts instead of strategy logic.
Comparing parameter sweeps when order handling differs across historical runs
Use a tool with explicit order handling such as Forex Strategy Builder so EA position lifecycle stays consistent across historical runs. Treat trade-level reporting as the consistency check when equity curve changes appear.
Assuming tick-by-tick modeling realism without validating tick data fidelity per symbol and date
QuantConnect tick-by-tick modeling depends on historical tick data availability and fidelity chosen for the run. Validate slippage, commission, and spread assumptions alongside the tick dataset used.
Mixing broker datasets without broker-aware ingestion and consistent report outputs
QuantRocket uses broker-aware historical data configuration and structured report exports to reduce dataset mismatch risk. Keep the broker data source configuration tied to each queued run so comparisons stay apples-to-apples.
Running strategy logic edits without checking trade-level diagnostics for subtle execution changes
Wealth-Lab trade-level reporting supports diagnosing logic changes that summary-only outputs hide. Use the same assumptions between iterations so edits map to EA logic behavior instead of data differences.
Expecting detailed latency or tick replay depth when using a platform-aligned tester with limited replay
cTrader Algo aligns with cTrader execution semantics inside Strategy Tester, but tick-level replay depth is limited compared with dedicated tick-data toolchains. Validate advanced execution effects like detailed latency modeling before relying on results for execution-sensitive decisions.
How We Selected and Ranked These Tools
We evaluated EA backtesting tools using execution modeling controls first because spread, commission, and slippage assumptions shift equity curve and drawdown outcomes. We then weighted automation and reporting workflow because queued runs, batch comparisons, and trade-level diagnostics determine whether parameter sweeps stay comparable.
We also weighted features and ease of use to measure how quickly teams can configure repeatable runs and interpret trade-level results. Forex Strategy Builder ranked highest because explicit order handling preserves EA position lifecycle through historical runs and the trade-by-trade reporting directly supports equity curve and drawdown analysis under consistent execution behavior.
Frequently Asked Questions About ea backtesting software
Which tools keep EA execution semantics aligned with the broker or platform they target?
How do bar-close modeling and tick-by-tick modeling differ across EA backtesting tools?
Which tool is better for batch-running many EA configurations across symbols and time windows?
How does broker-aware historical data setup impact EA test reproducibility?
What breaks when execution-cost modeling is missing or oversimplified?
How do data migration and format expectations differ between script-based and code-based backtesting tools?
Which tools provide tighter admin controls through project-level governance instead of manual reruns?
How do APIs and automation differ between cloud-first and workstation-first EA backtesting tools?
When should a team choose QuantConnect over a MetaTrader-aligned tester like Forex Tester or Forex Strategy Builder?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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