Optimizing TradingView Indicators with AI

👀 Mindset First: How AI reframes your trading

I used to think coding was the biggest barrier. If you wanted your own indicator with clear buy/sell signals and alerts, you had to spend hours learning Pine Script — or hire someone to do it for you.

But with AI, that wall disappears. Today, you can describe exactly what you want, when to enter, when to exit, and AI turns that into a working Pine Script strategy. No coding degree needed.

Instead of guessing, you become the architect of your own system. AI writes it, you test it, you tweak it, you make it yours.
You’re not chasing a holy grail. You’re building rules you can trust and repeat.

And just so we’re clear:

The model I’m about to share isn’t magic. I’ve studied setups from experienced traders and turned them into something that’s simple but workable.
Right now, it wins just over 30% of the time - enough to start learning and improving.

If this topic interests you, let me know. We’ll keep refining until we push the win rate closer to 90%.
Vote so we know you want more, and we’ll share every update.

🎨 How to Customize Charts Like a Pro

Below is a practical, AI-assisted workflow for a core TradingView indicator turned into strategies.

Williams Alligator trend strategy

After trying dozens of indicators, the Williams Alligator turned out to work best for this AI trading setup.
It was created by trader Bill Williams, and it’s called the “Alligator” because it uses three smooth moving averages that look like an alligator’s jaw, teeth, and lips.

Here's what makes it so special and intuitive:

• Jaw (Blue line): The slowest line. Think of it as the main trend. When it points up, the market’s long-term direction is bullish.

• Teeth (Red line): The medium line. It reacts faster and shows changes in the mid-term trend.

• Lips (Green line): The fastest line. It gives early buy and sell signals.

When the lines spread apart and move upward, the “alligator’s mouth” is open — that means an uptrend is strong.
When the lines cross and come together, the alligator is “sleeping,” and it’s better to stay out of trades.

What we want the system to do:

Right now, the Alligator is just showing us information on the chart.
We’re going to use ChatGPT to turn it into a full trading strategy that creates its own buy and sell signals and can be back-tested.

It’s like switching your car from manual to automatic. The car is the same, but now it’s much easier to drive.

Step by step:

1. Pull the Alligator source on TradingView. Click the { } icon to open Pine Editor. Copy the code.

2. Prompt ChatGPT to convert indicator to a strategy with precise constraints. Give it both the source and your rules. Keep language explicit, use Pine terms like “strategy.entry”, “strategy.exit”, “ta.crossover”, “ta.crossunder” to avoid ambiguity.

You are a skilled PineScript version=6 developer with expertise in coding indicators and strategies, and you understand their differences in code.

I need your help to convert a TradingView indicator into a strategy. Please follow these instructions:

1. Go Long when the green line (lips) crosses the blue line (jaw) to the upside.
2. Close Long when the green line (lips) crosses any of the other two lines to the downside.

Important Instructions:
- Convert all Indicator-specific code into Strategy-specific code. Do not use any code that is unsupported by TradingView Strategies, especially related to timeframes and gaps. Define those elements explicitly in the code to ensure they are semantically the same as in the original indicator.
  
- Retain the timeframe logic if it exists. Ensure gaps are filled.

- If the indicator is plotting something, the strategy code should also plot that same information to preserve the visuals. Set plot offsets to 0.

- Only go Long and Flat, never trigger a short position.

- Use the following strategy initialization code with all the default parameters:
strategy("NAME", overlay=true, calc_on_every_tick=false, initial_capital=1000, default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_type=strategy.commission.percent, commission_value=0.1, slippage=3, pyramiding=1, margin_long=0, margin_short=0, fill_orders_on_standard_ohlc=true)

- Do not declare 'strategy.commission.percent' or 'strategy.slippage' as separate variables. Instead, set them as arguments directly within the 'strategy()' function during initialization.

- Avoid making line breaks when calling functions, in IFs, Loops, or when defining Variables, as this will cause syntax errors. Line breaks are necessary only when defining new functions. Refer to the PineScript documentation for proper syntax.

- Leave all other strategy settings at default values (do not set them manually).

- Do not use `lookahead_on` because that is considered cheating.

- Add Start Date and End Date inputs to allow the user to specify the period for executing trades. Start with January 1st, 2018, and end on December 31st, 2069.

- When setting the strategy's title, prepend "AI - " to the name of the strategy.

This is the indicator code you need to convert into a TradingView Strategy:
[YOUR STRATEGY CODE GOES HERE]

3. Paste the returned strategy into Pine Editor and compile.

If errors show, screenshot or copy the error text and ask ChatGPT:

“what happened and how to fix it”

It will typically explain the errors and give you appropriate solutions. After that, just need to copy code fixed and replace.

4. Backtest and read the stats. 

Look first at Net Profit, Max Drawdown, Sharpe, and list of trades.

Identify the biggest losing trades and ask AI to add a parameterized rule to mitigate them.

For our biggest loser, the solution was clear: the trade needed a stop-loss. A stop-loss is an automatic exit order that gets you out of a losing trade before the loss becomes too large.

Action: Go back to ChatGPT and give it a new prompt:

This strategy is working well, but it has some large losing trades. Please add a stop-loss to the strategy to protect against these large losses. Use the ATR (Average True Range) indicator to create a dynamic stop-loss that adjusts to market volatility.

[PASTE YOUR CURRENT STRATEGY CODE HERE]

Final version:

//@version=6
strategy(title="AI - Williams Alligator Strategy",
         overlay=true,
         calc_on_every_tick=false,
         initial_capital=1000,
         default_qty_type=strategy.percent_of_equity,
         default_qty_value=100,
         commission_type=strategy.commission.percent,
         commission_value=0.1,
         slippage=3,
         pyramiding=1,
         margin_long=0,
         margin_short=0,
         fill_orders_on_standard_ohlc=true)

// ————————————————————
// Helpers
// ————————————————————
smma(src, len)=>
    sm = 0.0
    sm := na(sm[1]) ? ta.sma(src, len) : (sm[1] * (len - 1) + src) / len
    sm

// ————————————————————
// User inputs
// ————————————————————
jawLen   = input.int(13, title="Jaw Length",   minval=1)
teethLen = input.int(8,  title="Teeth Length", minval=1)
lipsLen  = input.int(5,  title="Lips Length",  minval=1)

jawOff   = input.int(8,  title="Jaw Offset")
teethOff = input.int(5,  title="Teeth Offset")
lipsOff  = input.int(3,  title="Lips Offset")

startD   = input.time(timestamp("2018-01-01T00:00:00"), title="Start Date")
endD     = input.time(timestamp("2069-12-31T23:59:59"), title="End Date")

// ATR-based stop-loss settings
atrLen   = input.int(14,  title="ATR Length",    minval=1)
atrMult  = input.float(2, title="ATR Multiplier", minval=0.1, step=0.1)

// ————————————————————
// Alligator lines
// ————————————————————
jaw   = smma(hl2, jawLen)
teeth = smma(hl2, teethLen)
lips  = smma(hl2, lipsLen)

// Preserve original visuals
plot(jaw,   "Jaw",   offset=jawOff,   color=#2962FF)
plot(teeth, "Teeth", offset=teethOff, color=#E91E63)
plot(lips,  "Lips",  offset=lipsOff,  color=#66BB6A)

// ————————————————————
// ATR calculation (volatility gauge)
// ————————————————————
atr = ta.atr(atrLen)

// ————————————————————
// Trade logic
// ————————————————————
inWindow  = (time >= startD) and (time <= endD)
longEntry = inWindow and ta.crossover(lips, jaw)
longExit  = inWindow and (ta.crossunder(lips, jaw))

if longEntry
    strategy.entry("Long", strategy.long)

if longExit
    strategy.close("Long")

// ————————————————————
// Dynamic ATR stop-loss
// ————————————————————
stopLevel = strategy.position_size > 0 ? strategy.position_avg_price - atr * atrMult : na
strategy.exit("ATR SL", from_entry="Long", stop=stopLevel, comment="ATR SL")

🔧 Iteration for Edge: Filters, stress tests, and risk

A. Add only what you can justify

Popular, orthogonal filters that play well with trend systems:

• RSI or Stochastic RSI to avoid overextended or listless zones.

• ADX to confirm trend strength and skip chop.

• SuperTrend as a trailing stop or exit confirm.

• Time filters to avoid weekends or certain hours on intraday crypto if your venue thins out.

Each filter should have an input toggle so you can A/B it quickly.

B. Stress tests to avoid self-deception

• Multi-asset test. BTC, ETH, SOL with unchanged logic and only minimal parameter ranges. If results collapse on non-BTC, you may be overfit.

• Multi-timeframe test. Daily and 4H. Expect different stats, but similar behavior.

• Repainting check. Favor standard indicators or audited code. Watch on 1–5 minute live charts to ensure past signals don’t shift.

C. Risk structuring

• Position sizing. Percent of equity is fine for testing. If you deploy, give yourself a fixed-dollar and risk-per-trade mode.

• Stops before filters. Exit fast on thesis violation, then add filters to reduce low-quality entries.

• Fee and slippage realism. Keep commission 0.1 percent and slippage 3 as your base. Tune up for illiquid pairs.

D. Fewer trades, more signal

The Gaussian + Stoch RSI example demonstrates the principle: fewer, higher-quality entries can hold net profit while reducing DD and fee burn. That’s your template for future improvements.

📊 Road to 90 percent win rate

It’s honest to say current win rate may start modest. If you’re into this topic, we’ll keep diving, tuning, and shipping versions until we crack stable 90 percent+ win conditions for specific regimes. The workflow above is how we’ll get there without curve-fitting ourselves into oblivion.