Turn Any YouTube Trading Strategy Into Working Code

The YouTube Strategy Problem

YouTube is flooded with trading strategy videos: "This indicator has 95% win rate!" or "The strategy that made me $100K!"

But there's a problem: watching isn't trading. You can't backtest a video. You can't automate a thumbnail.

This guide shows you how to extract actual trading rules from any video and convert them into testable, executable code.

Why 70% of YouTube Strategies Don't Work

Before we start, important context from quant researchers who've analyzed hundreds of YouTube strategies:

Common Problems

1. Cherry-picked examples: They show winning trades, hide losers
2. Incomplete rules: Entry shown, exit conditions vague
3. No risk management: Position sizing never mentioned
4. Curve-fitted parameters: "Use exactly 14.7 RSI" (suspicious precision)
5. Hindsight bias: Easy to see setups that already played out
6. Entertainment over education: Clicks > actual edge

Red Flags to Watch For

• "Works on any market, any timeframe"
• No losing trades shown
• Extremely high claimed win rate (85%+)
• Complex indicators with no explanation why
• "Just follow these signals"

The Extraction Process

Step 1: Get the Transcript

Most YouTube videos have auto-generated transcripts:

1. Open the video on YouTube
2. Click "..." below the video
3. Select "Show transcript"
4. Copy the full transcript

Alternative: Use AI tools like AssemblyAI or Whisper to transcribe with timestamps.

Step 2: Extract Trading Rules with AI

Use this prompt to extract structured rules from any transcript:

Analyze this trading strategy transcript and extract ONLY explicit trading rules.
Do NOT infer or assume anything not directly stated.

For each rule, specify:
- ENTRY LONG: [exact conditions]
- ENTRY SHORT: [exact conditions]
- EXIT LONG: [exact conditions]
- EXIT SHORT: [exact conditions]
- STOP LOSS: [exact method]
- TAKE PROFIT: [exact method]
- POSITION SIZE: [exact rules]
- TIMEFRAME: [specified timeframe]
- MARKETS: [specified markets]

If any component is not explicitly stated, write: "NOT SPECIFIED"

TRANSCRIPT:
[paste transcript here]

Step 3: Fill the Gaps

The AI output will show what's missing. For each "NOT SPECIFIED":

Option A: Watch the video again for visual clues Option B: Make a reasonable assumption and document it Option C: Test multiple variations

Common gaps to fill:

• Stop loss placement (if not mentioned, try ATR-based or swing low/high)
• Take profit rules (if not mentioned, try 1:1, 2:1, 3:1 R:R)
• Position sizing (if not mentioned, use fixed percentage risk)

Step 4: Convert to Pseudocode

Take the extracted rules and convert to platform-independent logic:

VARIABLES:
- fast_ema = EMA(close, 9)
- slow_ema = EMA(close, 21)
- rsi = RSI(close, 14)

ENTRY LONG:
- fast_ema crosses above slow_ema
- AND rsi > 50
- AND rsi < 70

EXIT LONG:
- fast_ema crosses below slow_ema
- OR stop loss hit
- OR take profit hit

STOP LOSS:
- 2 ATR below entry price

TAKE PROFIT:
- 2x stop loss distance (2:1 R:R)

POSITION SIZE:
- Risk 1% of account per trade

Step 5: Generate Platform-Specific Code

Use AI to convert pseudocode to your platform:

For Pine Script:

Convert this pseudocode to TradingView Pine Script v6.
Include strategy settings for backtesting.
Add all necessary variable declarations.
Make the code complete and ready to run.

[paste pseudocode]

For Python:

Convert this pseudocode to Python using pandas and numpy.
Structure it for backtesting with historical OHLCV data.
Include performance metrics calculation.

[paste pseudocode]

Complete Example: YouTube Strategy to Code

Original Video Claim

"EMA crossover with RSI filter - 75% win rate on ES futures!"

Extracted Rules (from transcript)

Entry Long:

• 9 EMA crosses above 21 EMA
• RSI(14) between 40-70 [vague, we'll use 50-70]
• 15-minute timeframe
• ES futures only

Exit Long:

• NOT SPECIFIED [we'll add EMA cross back]

Stop Loss:

• "Recent swing low" [vague, we'll use 2 ATR]

Take Profit:

• NOT SPECIFIED [we'll use 2:1 R:R]

Generated Pine Script

//@version=6
strategy("YouTube EMA-RSI Strategy", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=1)

// Inputs
fast_length = input.int(9, "Fast EMA")
slow_length = input.int(21, "Slow EMA")
rsi_length = input.int(14, "RSI Length")
rsi_lower = input.int(50, "RSI Lower")
rsi_upper = input.int(70, "RSI Upper")
atr_length = input.int(14, "ATR Length")
atr_mult = input.float(2.0, "ATR Multiplier")
rr_ratio = input.float(2.0, "Risk:Reward")

// Calculations
fast_ema = ta.ema(close, fast_length)
slow_ema = ta.ema(close, slow_length)
rsi = ta.rsi(close, rsi_length)
atr = ta.atr(atr_length)

// Signals
ema_cross_up = ta.crossover(fast_ema, slow_ema)
ema_cross_down = ta.crossunder(fast_ema, slow_ema)
rsi_ok = rsi > rsi_lower and rsi < rsi_upper

// Entry
if ema_cross_up and rsi_ok
    stop_distance = atr * atr_mult
    strategy.entry("Long", strategy.long)
    strategy.exit("Exit", "Long", stop=close - stop_distance, limit=close + stop_distance * rr_ratio)

// Exit on opposite signal
if ema_cross_down
    strategy.close("Long")

// Plots
plot(fast_ema, color=color.blue, title="Fast EMA")
plot(slow_ema, color=color.red, title="Slow EMA")

Backtest Results

Running this on ES futures 15m from 2023-2024:

Metric	Result
Net Profit	-$2,450
Win Rate	42%
Profit Factor	0.85
Max Drawdown	18%
Sharpe Ratio	-0.32

Reality check: The "75% win rate" strategy actually loses money when properly tested. This is typical.

Validation Framework

Before Trusting Results

1. Test on multiple timeframes: Does it only work on 15m?
2. Test on multiple assets: Does it only work on ES?
3. Test on different periods: Does it only work in 2023?
4. Walk-forward analysis: Does it work on unseen data?
5. Monte Carlo simulation: What's the range of outcomes?

Parameter Robustness

Change parameters slightly and retest:

• 9 EMA → 8 EMA, 10 EMA
• RSI 14 → 12, 16
• 2 ATR → 1.5 ATR, 2.5 ATR

If results collapse, strategy is likely curve-fitted.

RoboQuant Video-to-Code Feature

RoboQuant's AI can help automate this process:

1. Paste video transcript or describe what you saw
2. AI extracts rules and asks clarifying questions
3. Generates code for TradingView or Python
4. Backtest results immediately available
5. Identifies weaknesses in the strategy

Example Workflow

You: "I watched a video about buying when price bounces off the 200 SMA with bullish engulfing candle. They said to put stop below the candle and target 2R."

AI: "I'll create this strategy. A few clarifications:

1. Any timeframe preference? (I'll default to daily)
2. How close to the 200 SMA counts as 'bounce'? (I'll use within 0.5%)
3. Should we require uptrend confirmation? (200 SMA rising?)

Here's the initial code... [generates Pine Script]"

Why This Matters

The Knowledge Gap

Most traders:

• Watch videos ✓
• Think they understand ✓
• Never actually test ✗
• Lose money trading untested ideas ✗

The Solution

• Extract specific rules
• Code them explicitly
• Backtest objectively
• Discover most don't work
• Focus on what does

Conclusion

YouTube can be educational, but entertainment isn't an edge. Before trading any strategy you see online:

1. Extract the exact rules (use AI to help)
2. Fill in the gaps explicitly
3. Convert to testable code
4. Backtest properly
5. Expect most to fail
6. Be pleasantly surprised by the few that work

The goal isn't to prove YouTube gurus wrong—it's to find the occasional gem and build on it systematically.

Ready to test YouTube strategies properly? Try RoboQuant to convert video ideas to backtest-ready code in minutes.