summary: use TradingView's strategy report (formerly known as the strategy tester) to backtest edgeful algo indicators on continuous contracts, then evaluate results using net profit, drawdown, win rate, profit factor, and MAE — and use win rate ranges in your risk calculator to account for real-world variation.

heads up: the strategy report is a TradingView feature, not a separate edgeful indicator. you don't search for it under Invite-only scripts. it opens automatically when you apply any edgeful algo indicator (ORB, IB, gap fill, engulfing) to your chart. the algo is the indicator; the strategy report is the panel that shows you how it would have performed.

before you connect a broker and go live with an algo, you want to know how it's performed historically. TradingView's strategy report lets you run a backtest directly on edgeful's algo indicators so you can see real historical results on your instrument, timeframe, and settings.

this article covers how to set up a backtest, what to look at in your results, how to interpret win rates conservatively, and how to use metrics like MAE for deeper optimization before going live.

once you add an edgeful algo indicator to your TradingView chart, the strategy report opens automatically. you'll find it as a tab in the bottom-left corner of TradingView, alongside other panels like Pine Logs and Stock Screener. click the Strategy Tester tab (TradingView is rolling out the rename to Strategy Report, so you may see either name depending on your version) to bring it to the front.

if you don't see the Strategy Report tab, make sure you have one of edgeful's algo indicators active on the chart — standard study indicators won't trigger it.

running an algo on TradingView and optimizing it in the strategy report both depend on having the right TradingView data plan. without it, the strategy report uses delayed data and your backtest results won't reflect what live trading would look like.

you need both:

setup details for the CME data add-on: enabling real-time CME futures data on TradingView.

when backtesting, use continuous contracts (e.g. NQ1!, ES1!, MNQ1!). these give you uninterrupted historical data across contract rollovers, which gives you a more accurate picture of how the strategy would have performed.

when you're ready to trade live, switch to the active quarterly contract (e.g. NQM2026). continuous contracts are for testing only — prop firms and brokers use the quarterly contract for execution. the only time you continue to use the continuous contract for live trading would be accounts with ProjectX.

use the same chart timeframe you intend to trade on. most edgeful algo indicators are designed for specific timeframes — check the indicator settings for guidance on which timeframe to use.

the amount of historical data available to you depends on your TradingView plan. higher plans give you access to more bars, which means you can test further back in time. if you're on a lower plan and notice your backtest starts later than expected, upgrading your TradingView plan will extend your lookback range.

deep backtesting unlocks significantly more historical bars in the Strategy Report — which is what you need to backtest a 5-min algo over 12+ months of data.

there's no toggle to turn this on or off. if your TradingView plan is Premium or higher, deep backtesting is already active in the Strategy Report. you don't need to flip a setting, click a gear icon, or enable anything — apply an edgeful algo indicator, open the Strategy Report panel, and the extended bar range is already part of your backtest.

if you're on a lower plan (Free, Plus, or Essential), deep backtesting is not available. the only path to it is upgrading your TradingView plan to Premium or higher — there's no edgeful-side workaround.

how to tell whether you have it:

for more on what each TradingView plan tier unlocks (free vs. essential vs. premium), see hardware and software requirements for edgeful.

a common question — especially from traders who want maximum historical depth for backtesting but smaller per-contract risk for live trading.

short answer: backtest on the same contract family you'll trade live. NQ and MNQ track the same underlying index, so the strategy logic (entry signals, win rate, drawdown patterns) is essentially identical between the two. but the dollar figures in the strategy report scale with the contract's tick value (NQ = $20/point, MNQ = $2/point — exactly 10×). slippage profiles, liquidity, and broker execution dynamics also differ between the full-size and micro contracts.

practical workflow:

what not to do: don't take a backtest of NQ at full size, see the headline dollar figure, and assume that's what you'll see on a small funded account trading micros. the strategy report's PnL is tied to the contract you backtested at the size you tested — not to your actual account.

the same logic applies to ES → MES, YM → MYM, RTY → M2K, GC → MGC, and CL → MCL. backtest the family on continuous symbols if you need depth; final pass on the actual contract you'll trade.

don't just look at net profit. these are the key metrics that actually tell you if a strategy is worth trading:

MAE shows the maximum loss your position experienced before it closed — even if it eventually became profitable. a trade entry at 1.2000 that dips to 1.1950 before recovering to 1.2050 has an MAE of 50 pips. this metric reveals entry signal quality and stop-loss placement.

consistent MAE patterns reveal systematic issues with entry timing or position sizing. high MAE on winning trades suggests you're entering late or in noisy market conditions. low MAE on losing trades means your stops are working as designed.

step 1: run your strategy through TradingView's strategy report over 12+ months of historical data. note the average MAE for winning and losing trades separately.

step 2: compare MAE to your profit factor. high-profit strategies often have lower MAE because early entries lead to better risk/reward ratios.

step 3: if MAE is consistently high, adjust your entry conditions — add filters for volatility, trend alignment, or time-of-day. if MAE is high but losses are small, your stops are working; consider tighter stops.

MAE doesn't account for slippage or commissions — it's a pure price movement metric. in live trading, your actual max loss will be worse than MAE suggests. account for these costs when setting risk limits.

backtested win rates show historical performance but vary month to month. don't use a single number — use a range to plan for real-world outcomes in your risk calculator.

if your backtest shows a 66% win rate:

test both scenarios in the risk calculator. this gives you a realistic range of outcomes.

market conditions change, so your win rate will fluctuate. backtest monthly to monitor trends and adjust if needed. backtested win rates are inputs for your risk calculator — validate them regularly and refine your strategy based on what you observe over time.

it's common to run the same backtest a few weeks later and see different numbers. this is normal and expected — as time passes, new price data is added to the chart, which extends the tested period and updates the results.

this isn't a bug. it just means your backtest is always reflecting the most up-to-date data available. a strategy that looked one way in January will include February and March data when you run it again in March — so the numbers shift.

check your backtest results monthly. if you're not seeing any material changes to performance — win rate, drawdown, and profit factor are all holding steady — you can move to a quarterly review cadence.

if you notice a meaningful shift (e.g. a previously profitable session is now dragging results, or drawdown has increased significantly), that's a signal to revisit your settings before the next trading cycle.

overfitting is when you tweak your settings so specifically to past data that the strategy stops working in live conditions. to avoid it:

once you're happy with your backtest results, bring that data into the edgeful algo analyzer for a deeper optimization review before going live.

most people stop at the strategy report and call it done. that's leaving half the work on the table. the full optimization flow is:

deep dive on the algo analyzer: algo analyzer.