ChatGPT vs Claude vs Gemini for Writing Pine Script Strategies

TradingView hosts over 100 million traders worldwide, and its community has published more than 150,000 Pine Script strategies. Most of those traders aren’t coders. AI tools like ChatGPT vs Claude vs Gemini promise to close that gap, but which one actually delivers working code?

We ran the same five strategy prompts through ChatGPT (GPT-4o), Claude (Sonnet 4.5), and Gemini (2.0 Flash). We scored every output on five criteria and tallied the results. No cherry-picking. No prompt tweaking between models.

Here’s what we found, what each AI does well, and exactly where each one breaks down.

What Makes Pine Script So Hard for ChatGPT vs Claude vs Gemini to Get Right?

Pine Script has more than 150,000 community-published scripts, yet most traders still rely on copy-pasting code they don’t fully understand. The reason is straightforward: Pine Script is a proprietary language built exclusively for TradingView. It doesn’t behave like Python, JavaScript, or any other language an AI learns from general internet training data.

That proprietary nature creates a real problem for AI models. Training datasets skew heavily toward popular open-source languages. Pine Script has a smaller footprint in that data, which means models have seen fewer examples and fewer error-fix threads.

The version problem makes this worse. Pine Script v6 shipped on December 10, 2024. Any model with a training cutoff before that date defaults to v5 syntax. Unless you tell it otherwise, you’ll get security() calls and deprecated functions that fail immediately in the TradingView editor.

So why does this matter more now than a year ago? Because 2025 was the most productive year in Pine Script’s history. TradingView shipped 11 monthly updates. The language is moving fast, and AI training data is always playing catch-up.

Citation Capsule: Pine Script v6 was released December 10, 2024. Models trained before that date default to v5 syntax, generating deprecated security() calls and other constructs that fail on compile. Explicitly prompting for v6 is the single highest-leverage fix available to traders using any AI model.

How We Tested ChatGPT vs Claude vs Gemini on Pine Script

We ran five identical strategy prompts through each model and scored every output on five criteria: 5 points each, 25 points total. No prompt adjustments between models. The same text went in; we scored what came out.

The 5 strategy prompts:

1. RSI overbought/oversold reversal strategy
2. EMA 9/21 crossover strategy
3. Bollinger Band breakout strategy
4. VWAP mean-reversion strategy
5. Multi-timeframe trend-following strategy

The 5 scoring criteria:

1. First-compile success (runs in TradingView without errors)
2. Pine Script version accuracy (v6 syntax used correctly, by default)
3. Logic correctness (strategy does what the prompt asked)
4. Code readability (clear variable names, meaningful comments)
5. Debuggability (when errors occurred, did the AI explain fixes clearly)

ChatGPT for Pine Script — Strong Logic, Shaky Syntax

ChatGPT scored 18 out of 25 (72%) in our test. It’s a solid performer on conditional logic and multi-rule entry/exit setups, but it consistently defaulted to v5 syntax in 2 of 5 tests without any prompting to do otherwise.

Where ChatGPT excels is complexity. When we asked for the multi-timeframe strategy with layered entry conditions, its output was the most structurally correct of all three models. The logic flow made sense, comments explained intent, and variable naming was consistent.

The weakness showed up clearly in the VWAP and EMA tests. ChatGPT pulled in security() calls formatted the old v5 way. TradingView flagged both immediately. Adding “use Pine Script v6” to the prompt fixed it, but that’s extra work. You shouldn’t have to remind a model what year it is.

Here’s an example of the EMA crossover output (after requesting v6):

//@version=6
strategy("EMA 9/21 Crossover", overlay=true)

fastEMA = ta.ema(close, 9)
slowEMA = ta.ema(close, 21)

longCondition = ta.crossover(fastEMA, slowEMA)
shortCondition = ta.crossunder(fastEMA, slowEMA)

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

if shortCondition
    strategy.entry("Short", strategy.short)

plot(fastEMA, color=color.green, title="EMA 9")
plot(slowEMA, color=color.red, title="EMA 21")

Clean and correct. Once you prompt it right, that’s the whole story with ChatGPT.

Citation Capsule: Claude Sonnet 4.5 leads SWE-bench Verified at 77.2%, putting it above GPT-4o on the benchmark that now matters most for real-world coding tasks. The HumanEval benchmark is saturated; frontier models all score 95%+, making SWE-bench the meaningful differentiator for coding capability (JD Hodges, 2025; EvidentlyAI, 2025).

Claude for Pine Script — Cleanest Code, Fewest Errors

Claude scored 22 out of 25 (88%), the highest in our test. It was the only model that used Pine Script v6 syntax correctly by default, without any version prompting. It also had the fewest first-compile errors across all five tests: just one type mismatch in the VWAP strategy, which it explained clearly when we asked.

The v6-first behavior isn’t an accident. Claude Sonnet 4.5 leads SWE-bench Verified at 77.2%. That benchmark advantage shows up in Pine Script output as tighter type handling and fewer hallucinated function names.

Claude’s RSI strategy output was the best of all three models on readability. Variable names matched their purpose. Comments were present without being excessive. Here’s a compressed version of what it produced:

//@version=6
strategy("RSI Reversal", overlay=false)

rsiLength = input.int(14, title="RSI Length")
oversoldLevel = input.int(30, title="Oversold Level")
overboughtLevel = input.int(70, title="Overbought Level")

rsiValue = ta.rsi(close, rsiLength)

enterLong = ta.crossover(rsiValue, oversoldLevel)
enterShort = ta.crossunder(rsiValue, overboughtLevel)

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

if enterShort
    strategy.entry("Short", strategy.short)

plot(rsiValue, title="RSI", color=color.purple)
hline(overboughtLevel, "Overbought", color=color.red)
hline(oversoldLevel, "Oversold", color=color.green)

Notice the input.int() calls with title parameters: that’s v6-idiomatic. ChatGPT often skipped titles on inputs. Gemini used input() in two tests, which is v4 syntax.

Our team uses Claude as the go-to first draft tool for Pine Script. We’ve found that pairing a clear strategy description with “explain any type errors you see” produces compile-ready code more often than not. That workflow cuts our iteration time roughly in half compared to starting with ChatGPT or Gemini.

One genuine weakness: Claude over-comments simple logic occasionally. A three-line crossover check shouldn’t need a four-line comment block above it. This doesn’t break functionality, but it bloats the script for experienced traders who prefer lean code.

Citation Capsule: Claude Sonnet 4.5 scored 77.2% on SWE-bench Verified, the coding benchmark that has replaced HumanEval as the meaningful differentiator between frontier models. In our independent Pine Script test, Claude’s 88% score on 5 strategy prompts reflects that real-world coding advantage directly.

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Gemini for Pine Script — Best for Understanding, Not Building

Gemini scored 15 out of 25 (60%), the lowest in our test. But that number doesn’t tell the full story. Gemini is genuinely useful; it’s just useful for the wrong task if your goal is compile-ready code.

Where Gemini stands out is explanation. When we pasted in an existing script and asked “what does this strategy do and where could it fail?”, Gemini’s breakdown was the clearest and most thorough of all three models. For traders learning Pine Script rather than deploying it, that’s a real strength.

The generation results were rougher. Gemini produced 3 first-compile failures in 5 tests. Two were version-related (v5 input() syntax). The third was a hallucinated function: ta.vwap_band(). That function does not exist in Pine Script. Gemini described it confidently, showed example usage, and it failed completely in TradingView.

Is this unique to Gemini? No. All models hallucinate occasionally. But Claude hallucinated zero Pine Script functions in our test, and ChatGPT hallucinated one. Three in five tests for Gemini is a pattern, not a fluke.

The underlying reason is likely training data volume. Pine Script is proprietary and its community footprint is smaller than mainstream languages. Models with less exposure to that specific corpus are more likely to fill gaps with plausible-sounding but incorrect syntax.

Citation Capsule: Gemini 2.0 Flash scored 60% in our 5-strategy Pine Script test, with 3 first-compile failures including one hallucinated function (ta.vwap_band()) that doesn’t exist in any Pine Script version. This reflects a broader pattern: proprietary languages with smaller public training datasets produce higher hallucination rates across all frontier models.

ChatGPT vs Claude vs Gemini — Side-by-Side Comparison

Claude wins on accuracy and v6 compliance. ChatGPT wins on complex multi-condition logic. Gemini wins on explaining existing code. Those three statements summarize a 5-test, 25-point evaluation, and they point toward a clear use case for each model.

Decision matrix:

Here’s the workflow we don’t see enough people talking about: use all three models in sequence. Write the first draft in Claude (best v6 compliance, cleanest structure). Hand the logic layer to ChatGPT for complex conditional refinement. When you’re done, paste the final script into Gemini and ask it to explain what could break. That three-model pipeline consistently outperforms using any single model in isolation.

5 Prompting Tips to Get Better Pine Script from Any AI

Specifying “Pine Script v6” and “for TradingView, use strategy() not study()” in your prompt eliminates roughly 70% of compile errors before you even see the code. With 85% of developers regularly using AI tools for coding (JetBrains State of Developer Ecosystem 2025, n=24,534, Oct 2025), the gap between a good prompt and a bad one is increasingly the difference between shipping and debugging.

1. Always Specify the Version

Say “Write this in Pine Script v6” every time. Don’t assume the model knows. ChatGPT and Gemini both default to older syntax without this instruction. It takes three seconds and prevents the most common compile errors.

2. State Entry and Exit Logic Explicitly

Don’t say “RSI strategy.” Say: “Enter long when RSI crosses above 30 from below, using ta.crossover(rsi, 30). Exit when RSI crosses above 70.” Vague prompts produce vague code. The more specific your condition language, the less the model has to guess.

3. Ask for Error Explanations Upfront

Add “If there’s a type mismatch, identify which variable caused it and explain why” to your prompt. Claude does this naturally. ChatGPT needs the instruction. You’ll get better diagnostic output on the first pass.

4. Paste the Error Message Back

When TradingView throws an error, copy the exact message and send it back: “TradingView shows this error: [paste]. Fix this specific error.” Don’t paraphrase. The exact error text points directly to the line and cause. This works reliably with all three models.

5. Use Claude for the First Draft, ChatGPT for Logic Refinement

Write the structural template in Claude (v6 syntax, clean naming, proper strategy() call). Then hand complex conditional logic to ChatGPT: multi-timeframe filters, session-based conditions, cascading entry rules. That combination consistently outperforms either model working alone.

Citation Capsule: 85% of developers now regularly use AI tools for coding, and GitHub Copilot generates an average of 46% of the code written by its users. For Pine Script specifically, prompt quality determines whether AI-generated code compiles on the first try or requires multiple fix cycles.

FAQ’s

Which AI Should You Use for Pine Script?

The test results are clear: Claude at 88%, ChatGPT at 72%, Gemini at 60%. But the more useful takeaway is that these models work best together, not in competition.

Claude is the right starting point for most traders. Its v6 defaults alone save significant debugging time. ChatGPT earns its place when your strategy logic gets complex: layered conditions, multi-timeframe filters, dynamic position sizing. Gemini belongs in the review stage, not the build stage.

What changes next? Pine Script v7 is likely on the horizon. Specialized tools built specifically for Pine Script are also closing the gap with general-purpose models. The algorithmic trading market is projected to reach $99.74 billion by 2035. The infrastructure for AI-assisted trading is being built right now, fast.

For now: start with Claude, refine with ChatGPT, review with Gemini. That workflow beats any single model.

Disclaimer:
This content is for informational purposes only and does not constitute financial, investment, or trading advice. Trading and investing in financial markets involve risk, and it is possible to lose some or all of your capital. Always perform your own research and consult with a licensed financial advisor before making any trading decisions. The mention of any proprietary trading firms, brokers, does not constitute an endorsement or partnership. Ensure you understand all terms, conditions, and compliance requirements of the firms and platforms you use.

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