summary: use TradingView's 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.

before you connect a broker and go live with an algo, you want to know how it's performed historically. TradingView's strategy tester 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've added an edgeful algo indicator to your TradingView chart, the strategy tester panel appears at the bottom of the screen. click the Strategy Tester tab to open it.

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

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.

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 tester 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.