the algo analyzer takes your TradingView backtest data and turns it into a full breakdown of your strategy's performance — strategic health score, Monte Carlo simulations, prop firm pass rate projections, and optimization suggestions, all in one place.

before this tool existed, the process meant exporting an XLSX from TradingView and doing all the calculations manually in Excel. the analyzer handles all of that for you.

the algo analyzer is included in the all access plan — edgeful's algo plan.

the algo analyzer is in the algos section of your edgeful account.

direct link: edgeful.com/algos-automation/algo-analyzer

when you first open it, you'll see a single option — upload your backtest file from TradingView. once you've uploaded at least one, your saved runs will appear on the dashboard.

run your backtest in TradingView's strategy tester with the settings you want to analyze. we'll use a 5m ORB strategy as the example throughout this article.

to export: expand the strategy tester panel at the bottom of TradingView, click the strategy name (e.g. edgeful - algos - ORB strategy), then select download data as XLSX from the dropdown.

TradingView will notify you when the download is ready.

not sure where the file saved? open TradingView settings (3 dots in the top right → settings, or CTRL + ,) and check the general tab — your download location is listed there.

if you are using a web browser and not the TradingView desktop app, the XLSX will be saved in your browser's download save location.

back in edgeful, click the upload button in the algo analyzer and find the XLSX file you just exported from TradingView.

the analyzer will process it immediately. here's what the upload screen looks like:

once the file loads, you'll land on the analysis tab. the strategy name, trade count, and date range sit at the top — you can rename it by clicking the edit name button, and star it to pin it to the top of your dashboard.

by default all sections are visible, but you can customize what's shown — click customize and toggle sections on or off.

a snapshot of the key metrics from your backtest — net profit, win rate, profit factor, Sharpe ratio, max drawdown, and average return.

3 views in one section: the equity curve over your tested date range, a P&L breakdown by month so you can see your best and worst months at a glance, and a trade performance summary — average win, best win, average win streak, and the same for losses.

this is where the analyzer earns its keep — it checks your strategy against 6 criteria and flags anything that needs attention.

each check comes back as a pass, caution, or issue, so you know exactly what needs your attention.

the 6 checks:

based on the analysis, this section gives you specific suggestions — what to turn off, what to tighten, and what to adjust — to improve your results.

each suggestion has 3 parts worth reading closely:

the reason is the most important part. a suggestion with weak underlying stats (small sample, marginal edge, one big outlier driving the number) is less reliable than one with a clear pattern across a large sample.

when there are multiple suggestions, work in this order:

the analyzer is doing statistical pattern matching on your backtest data. it can't see context it doesn't have. a few times you should trust your own read over the suggestion:

the analyzer is a feedback loop, not a one-time check. the single biggest mistake traders make is changing 3 things at once, then not knowing which change helped.

the right process:

this takes longer than bulk-changing 5 parameters at once. but after 5-10 iterations, you'll have a strategy where you know exactly why every setting is the way it is — and the confidence that comes with that matters when you're live.

if you're preparing for a prop firm challenge, add these steps to the loop:

keep iterating until the strategic health checks are mostly passing and the Monte Carlo / prop firm simulator results look solid across multiple runs. that's when you're ready to connect a broker and go live.

it takes time. but it's significantly better than finding out your strategy breaches drawdown limits on day 3 of a live challenge.

3 breakdowns that help you dig into the specifics:

weekday × direction breakdown — P&L, wins, losses, win rate, and trade count separated by day of week and long vs. short. use this to identify which days are dragging results and whether it's your longs or shorts that are underperforming.

drawdown analysis — optimal max loss — helps you find the best max loss setting based on actual trade data. it shows the average drawdown of winning trades and the average run-up of losing trades, so you're not cutting winners short or letting losers run too far.

time of day — breaks down entry times and the typical profit associated with each. toggle between a 15m and 30m view to fine-tune your entry window.

every trade listed in full detail — sortable and filterable so you can dig into specific dates, sessions, or outcomes.

a quick-reference list of all the algo settings from your TradingView indicator's properties tab — useful when you're comparing runs and want to know exactly what settings each one was using.

the analyzer handles 95%+ of what most traders need. the cases below are where Excel still earns its keep.

the quick rule: if the question can be answered from one of the analyzer's tabs, use the analyzer. if you need to tag or slice the data by your own criteria, or compare across strategies / tickers, export the XLSX and work in Excel.

most members end up using both — the analyzer for the standard questions, Excel for the specific ones.

a Monte Carlo simulation runs your strategy through thousands of randomized trade sequences to show you the range of possible outcomes — not just what happened historically, but what could realistically happen going forward.

an overview of the simulation results — whether the strategy is profitable across the simulated paths and how it's likely to perform.

shows all simulated equity paths at once. you can adjust the number of paths — 100, 250, 500, or 1000 — using the dropdown in the top right corner of the chart.

shows how final P&L is distributed across 1000 simulated paths — and marks where your actual backtest result sits within that distribution. if your result is near the top of the distribution, that's worth noting.

risk of ruin is the likelihood your account loses enough capital that you can't recover — not just a losing streak, but an account-ending event.

this section shows how likely specific drawdown levels are to occur across the simulations. select one of the preset drawdown toggles or enter a custom amount — the analyzer will show you how often that level was hit across all simulated paths.

you'll also see the median worst drawdown and the worst 5% drawdown — two useful anchors for understanding realistic downside scenarios.

this section explains the methodology behind the Monte Carlo simulation — useful if you want to understand how the numbers are being generated.

the analysis tab and Monte Carlo tab tell you how your strategy performs in general — win rate, profit factor, drawdown, the range of possible outcomes. those are health metrics.

the prop firm simulator goes a step further. it takes that same backtest data and runs Monte Carlo simulations against your specific prop firm's rules — drawdown limits, profit targets, and challenge duration.

so the analyzer might say "this is a solid strategy" while the simulator says "but it'll breach your firm's daily drawdown limit 40% of the time." both can be true — they're measuring different things.

you'll need:

if you haven't uploaded a backtest yet, scroll up to the "exporting your backtest from TradingView" section above.

start by selecting your prop firm and profit target. the simulator will run Monte Carlo projections against that firm's specific rule set.

the simulator runs multiple Monte Carlo paths using your backtest data — essentially asking "if this strategy ran hundreds of times under these rules, how often would it pass?"

the dropdown includes a number of popular prop firms with their rules pre-loaded — but we know there are a lot of firms out there, and yours might not be on the list yet.

if that's the case, reach out to our support team and let us know which firm you're trading with. we're actively adding new firms, and if there's demand for yours, we'll get it added to the preset list. you can reach us through the chat widget inside edgeful or by sending an email to support.

your overall pass rate as a percentage. this is the headline number — if it says 72%, that means in the simulated runs, your strategy passed the challenge about 7 out of 10 times.

a chart showing all simulated evaluation runs — how many passed, how many failed, and where they diverged. green paths passed, red paths failed. adjust the number of paths (100, 250, 500, or 1000) with the dropdown in the top right.

this gives you a visual feel for how much variance exists in your strategy's outcomes.

shows how many simulated accounts are still active (not breached) at each point in the evaluation — so you can see whether most failures happen early or late in the challenge.

if failures cluster early, your strategy may be too aggressive at the start. if they cluster late, you might be grinding too slowly and running out of time.

when simulated accounts fail, this section shows why — which rule was breached and how often. is it the daily drawdown? the trailing drawdown? the profit target (not hitting it in time)?

this is where you find the specific thing to fix.

there's also a methodology explainer at the bottom of the tab for a deeper look at how the prop firm simulator calculates its projections.

this is the most common question — and it makes total sense once you see why.

the analysis tab evaluates raw performance. a strategy with a 65% win rate, 1.8 profit factor, and manageable drawdown looks good on paper.

but the prop firm simulator applies constraints on top of that performance. your firm might have a 4% trailing drawdown limit. your strategy might have a max drawdown of 3.5% historically — looks fine. but Monte Carlo simulation shows that in 30% of runs, normal variance pushes that drawdown past 4%.

that's the gap. historical performance is one path. the simulator shows you the range of possible paths — and some of those paths breach your firm's rules even with a "good" strategy.

so what do you do? adjust. tighten your stop loss, reduce position size, or use the day-of-week filters to avoid the highest-variance days. then re-run the simulator until your pass rate is where you want it.

every strategy you upload gets saved to the dashboard. you can star your best-performing runs to pin them to the top, rename them to stay organized, and delete old ones you no longer need.