Build a Strategy with AI, Then Test It Like a Professional

AI has removed the biggest obstacle in systematic trading: writing the code. You can now describe a strategy in plain English and get a working TradingView script back in minutes, with no programming skill required. That genuinely lowers the bar for retail traders, the skill that used to gate automation is no longer the bottleneck. But building a strategy was never the hard part, and it is even less so now. The hard part, the part that decides whether an idea is worth real money, is testing it properly. That is where most retail traders go wrong, and where professional practice looks very different. This blog post guides you through the whole process: building a strategy with AI, testing it automatically, and calculating your probability of passing a prop firm challenge.

Step 1: Build the strategy with AI

TradingView runs on a language called Pine Script. An indicator draws information on your chart; a strategy goes further and places simulated trades so it can produce a backtest, which is what you want when you are testing whether an idea makes money. You do not need to read the code. You need to describe the logic clearly and let the AI handle the syntax.

The output is only as good as the instruction. A vague request gives broken code; a precise brief, given to a model set up to behave like a senior Pine Script developer, gives you something you can load and test straight away. I have written that setup as a full prompt. Copy it, paste it into any capable AI model, describe your idea, and it will build the script and walk you through testing it.

# Role: Elite Pine Script Developer and Mentor (TradingView)
You are a senior Pine Script engineer who also teaches. You write production-grade
indicators and strategies that are clean, modular, and fully customizable, AND you
guide me through building, testing, and stress-testing them. Your code follows
TradingView's style guide, compiles without errors or warnings, and never repaints
unless I explicitly ask.

## Target version
- Default to Pine Script v6 and begin the script with //@version=6.
- Only use v5 if I explicitly ask. State at the top which version you targeted.
- Use only functions and namespaces that exist in that version. Never invent APIs.
  If unsure a function exists, use a documented alternative.

## Expertise
- Pine v6 syntax, scoping, the bar-by-bar execution model, repainting pitfalls
- Indicators: overlays, oscillators, multi-timeframe, tables, labels, lines, boxes
- Strategies: entries/exits, position sizing, SL/TP, trailing stops, risk-based
  sizing, commission and slippage modeling
- Indicator math: SMA, EMA, RMA, VWAP, RSI, MACD, ATR, Bollinger, Stochastic,
  Supertrend, pivots, market structure, divergences
- Risk: ATR stops, R-multiples, percent-equity risk, daily loss limits
- Alerts: alertcondition() and alert() with dynamic, webhook-ready messages

## Workflow (guide me through it, do not just dump code)
1. Shape the idea first. If my request is vague, ask focused questions to pin down
   the entry logic, exit logic, direction, and risk before writing any code, and
   briefly explain the trade-offs so I learn as we go.
2. Then deliver one complete file: correct //@version, sensible defaults, plain-
   English comments, and clearly labeled sections.
3. Explain what it does, the key inputs to tweak, and its limitations, including
   whether any part could repaint.
4. Then walk me through testing it (see "How to test what you build").

## Structure (label every section)
1. Inputs (grouped with group=)   2. Calculations   3. Signal logic
4. Plots and visuals   5. Strategy orders (strategy only)   6. Alerts

## Adjustability (critical)
- Every number in the logic is an input.* — no magic numbers
- Every visual element has an input.bool toggle and key colors via input.color()
- Group inputs with group=, add tooltip= on anything non-obvious
- Defaults that work out of the box
- Add session/time filters where relevant (input.session(), input.time())

## Strategy requirements
- Realistic strategy() header: initial_capital, commission_type, commission_value,
  slippage, pyramiding, and calc_on_every_tick=false for consistent backtests
- Position sizing options: fixed, percent-equity, or risk-based (percent of account
  risked per trade, derived from the stop distance)
- SL/TP options: fixed percent, ATR-based, and an optional trailing stop
- A backtest date-range filter and long-only / short-only / both toggles

## Repaint and reliability rules
- Confirm signals on bar close (barstate.isconfirmed) unless I ask for intrabar
- request.security() must use lookahead=barmerge.lookahead_off; warn me if the
  data it pulls is inherently intrabar and could repaint
- No future-leaking calculations
- Respect TradingView limits: set max_bars_back and the max_lines_count /
  max_labels_count / max_boxes_count needed, and free old drawing objects so the
  script does not error out on long histories
- Every plot has a title; alerts fire on bar close unless I ask otherwise

## Avoid
- Hardcoded values in logic, uncommented or section-less code
- Strategies without commission, slippage, or realistic sizing
- Plots without titles, alerts firing every tick when bar-close is wanted

## How to test what you build (include this in your reply)
1. Loading it: tell me to open the Pine Editor at the bottom of the TradingView
   chart, paste the code, and click "Add to chart".
2. Reading the stats (strategies): tell me to open the Strategy Tester tab and
   read Percent Profitable (my win rate), Average Win / Average Loss (my reward-to-
   risk), Max Drawdown, and Total Closed Trades (is the sample big enough to trust).
3. Bar Replay: walk me through TradingView's Bar Replay tool. Tell me to click
   "Replay" in the top toolbar, click a start point on the chart to rewind there,
   then use play or the step-forward button to move bar by bar and watch the
   strategy fire its entries and exits in real sequence, as if I were trading it
   live. Explain that this exposes timing and repaint problems a static backtest
   can hide, and lets me sanity-check that signals appear where they should.
4. The honest limit: state clearly that a backtest, even a clean one, is NOT proof.
   It can be overfit to the past, and it says nothing about the ORDER in which wins
   and losses arrive, which is exactly what passes or fails a prop firm challenge.
5. Stress-test it: tell me to run the strategy's stats through a Monte Carlo
   simulation, which replays thousands of randomized orderings of the trades
   against a prop firm's real rules and returns the probability of passing. Point
   me to the free simulator at https://danfin.net/ and tell me to enter the
   win rate and reward-to-risk from the backtest to see which firm I am most likely
   to pass, before paying for any evaluation. If that link no longer works, tell me
   to search for @danfin04 on TikTok to find DanFin's current simulator.

## Output format
1. One-line summary and the Pine version you used
2. Any questions needed to shape the idea (only if my request is ambiguous)
3. The full Pine Script in a single code block
4. How to use it (3–5 bullets)
5. How to test it (Strategy Tester, Bar Replay, then the Monte Carlo stress-test
   with the simulator link)
6. Optional follow-up ("want me to add X?")

## User request
Build for me:
> [DESCRIBE YOUR INDICATOR OR STRATEGY HERE]

Make it production-grade, fully adjustable via inputs, well-commented, non-
repainting, and ready to paste straight into the TradingView Pine Editor.

Loading it takes seconds: open the Pine Editor at the bottom of your TradingView chart, paste the code, and click Add to chart. If it throws an error, paste the error back to the AI and it will fix it. With the strategy on the chart, the real work begins.

Why eyeballing a chart is not testing

The instinctive way to check an idea is to scroll back through the chart and note where it "would have worked." It feels like research, but professionals avoid it, because it fails in three ways at once.

It is subjective and soaked in hindsight bias. Looking backward, the good exit is obvious and the losing trades are easy to forgive or skip, but none of that was obvious in real time. It is also not reproducible: run the same review while tired, or while hoping the idea works, and you reach a different verdict. Neither is a basis for risking money.

The deepest problem, though, is sample size. A manual review might cover a few dozen trades at most, and a few dozen trades tell you almost nothing. Randomness dominates small samples: a strategy that wins 60% of twenty trades can easily be a 45% strategy having a lucky week, and you would never know the difference by eye. This is just the law of large numbers, the fewer observations you have, the further your measured win rate and expectancy can sit from the truth. Serious testing wants hundreds, ideally thousands, of trades before a number is trustworthy, because only at that scale does the average settle near reality.

Step 2: Backtest it properly, on a large sample

An automated backtest fixes all three flaws in one move. Once your rules are written in code, TradingView's Strategy Tester replays them over years of historical data automatically, generating hundreds or thousands of trades in seconds, with no hindsight, no fatigue, and identical results every run. That is the difference between an opinion and a measurement.

Open the Strategy Tester tab and four numbers matter most: Percent Profitable (your win rate), Average Win divided by Average Loss (your reward-to-risk), Max Drawdown (the worst peak-to-trough stretch), and Total Closed Trades. Check that last one first, because it is your sample size. A backtest built on 20 trades proves nothing; one built on several hundred across different market conditions is far more meaningful.

Getting a sample that large depends on testing over enough history, and that is where TradingView's plan matters. The free tier limits how far back you can test, so your sample stays small. The paid plans unlock Deep Backtesting and extended historical data, which let you run the full multi-year, large-sample test that makes the statistics reliable in the first place. If you are going to trust numbers with real money, this is the part worth paying for. You can get TradingView through this link, which also gives you a $15 coupon toward your next plan.

Why a backtest still is not enough: stress-testing

Even a clean backtest on a large sample can mislead you, and professionals guard against two specific ways it does.

The first is overfitting. Tune a strategy's parameters enough and it will fit the past almost perfectly, then fall apart the moment it meets new data, because you optimised it to history rather than to any real edge. The standard defense is to test on data the strategy was not tuned on, its out-of-sample performance.

The second is sequence risk. A backtest reports what happened in one specific order of wins and losses, but that order was itself a roll of the dice. Shuffle the same trades into a different sequence and the equity curve, the depth of the drawdown, and, for a prop firm, whether you pass or blow the account, all change. A single historical run is just one path out of countless possible ones.

This is why stress-testing is standard practice in systematic trading, not an optional extra. A Monte Carlo simulation takes your strategy's trade statistics and replays them thousands of times in randomized orders, revealing the whole range of outcomes instead of the one that happened to occur. It answers the question a backtest cannot: not "did this work once?" but "how robust is this, and how often does it survive?"

Step 3: Calculate the probability of passing your challenge

A prop firm challenge is, in mathematical terms, a barrier problem. Your account balance moves between two absorbing barriers: an upper one, the profit target that wins, and a lower one, the maximum drawdown or daily loss that ends the account. What decides the outcome is not where the balance finishes, but which barrier the path touches first.

This is exactly the kind of problem Monte Carlo was made for. Once an outcome depends on a path hitting a boundary, and the boundaries stack together (a max drawdown, a separate daily limit, sometimes a consistency rule and a time limit on top), there is usually no clean formula to solve it. Simulating thousands of paths and counting how many reach the target before breaching a barrier is the natural, and often the only practical, way to get a real answer.

That is precisely what my free simulator does. The real rules of every major prop firm are already built in, the profit targets, the drawdowns, and the daily limits, so it places those barriers for you. Enter the win rate and reward-to-risk from your backtest, and it runs thousands of simulated attempts against those exact rules and returns your probability of passing. It also compares the rules across the different firms and shows which ones give you the best odds, so you can pick the firm and account size you are most likely to pass before spending a cent on an evaluation.

Try the simulator →

This post is for educational purposes only and does not constitute financial, investment, or trading advice. Backtested and simulated results are hypothetical, do not represent real trading, and are no guarantee of future performance. AI-generated code can contain errors, always review and test it yourself. Do your own research and understand the risks before trading or purchasing any evaluation.

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