Opening: Let’s Talk About the Hassles of Validating Trading Ideas

Hey, I wonder if anyone else has had this experience: you’re watching the market at night and suddenly come up with a trading idea, like noticing certain common characteristics before some coins pump. Then you want to verify whether this idea actually works, but you find out you need to write code, deal with data, and all that stuff - it’s such a hassle. By the time you actually get around to doing it, either you’ve forgotten what you were thinking, or the market conditions have already changed.

How Torturous the Traditional Validation Process Is

To be honest, validating an idea used to be really exhausting. First, you have to get the data - finding various API interfaces, registering accounts, applying for API keys and such, and then writing code to scrape the data. This step alone is enough to keep you busy for ages. Then you have to turn that fuzzy idea in your head into specific calculation formulas and write them into code. Finally, you need to do backtesting, considering all sorts of messy things like transaction fees and slippage. The whole process takes half a day if you’re lucky, but could take several days if you’re not.

Trading Ideas Have an Expiration Date Too

There’s a problem here - good trading ideas actually have a shelf life. Especially in crypto, things change incredibly fast. A pattern you discover today might stop working in a week or two. But traditional validation methods are too slow to keep up. Plus, inspiration doesn’t come at convenient times - it often strikes when you’re on the subway or taking a shower. You can’t exactly start coding right then and there, can you?

The Simple Validation Method I Want

So I started thinking, couldn’t we make validating ideas simpler? I mean, I just need to describe my idea in words, and let the tools handle the rest. No coding, no data wrangling, and when the results come out, they directly tell me whether it works or not. Basically: I do the thinking, the machine does the validation.

Implementing It with FMZ Workflow

Later, I built an automated workflow using FMZ (Inventor Quantitative). FMZ’s workflow is specifically customized for quantitative trading and can chain various tools together. The entire process works like this: when inspiration strikes, you simply open your phone and input your factor description. Then the AI converts this description into executable code, automatically retrieves cryptocurrency data from the FMZ platform, performs factor validation calculations, and finally the AI translates the results into plain language and sends them to you. The whole process is completely automated - you just need to wait for the results.

Factor Validation Flowchart

flowchart TD
    A[📱 Input Idea via Telegram] --> B[🧠 AI Understands Factor Description]
    B --> C[💻 Generate JavaScript Code]
    C --> D[📊 Retrieve Cryptocurrency Data]
    D --> E{🔍 Data Check}
    E -->|Sufficient Data| F[⚙️ Factor Calculation]
    E -->|Insufficient Data| Z[❌ Return Error]
    F --> G[📈 IC Analysis]
    F --> H[📉 Monotonicity Analysis]
    F --> I[⏱️ Decay Analysis]
    F --> J[💰 Cost Analysis]
    G --> K[🤖 AI Interprets Results]
    H --> K
    I --> K
    J --> K
    K --> L[📋 Generate Evaluation Report]
    L --> M[📲 Push Results via Telegram]

The Setup Process Isn’t Actually That Complex

Setting up this workflow isn’t really that complicated. It mainly requires a few steps: First, configure the AI model’s API in the workflow - here I used OpenRouter’s interface, which can call large models like DeepSeek. Then configure the FMZ platform’s data interface to retrieve K-line data. The most critical part is writing the logic code for factor validation, including various statistical tests and monotonicity analysis. Don’t worry if you can’t understand these professional analysis results - we’ll have the AI explain them to us and directly tell us whether this factor has potential or not. Finally, configure a message push to send the results to Telegram.

What the Validation Results Can Tell You

When you open this analysis report, there’s actually quite a lot of information. First is an overall score and rating that lets you see at a glance whether this idea is reliable. Then there’s the specific factor construction code. This part is particularly valuable because the AI-generated code is very well-structured with detailed comments. You can see how it calculates things, with every step clearly written out. Even if you can’t code, after seeing enough of these, you’ll understand the logic of factor construction. This is very helpful for learning quantitative trading - it’s like having an AI tutor teaching you how to write factor code step by step.

The report also includes interpretations of various performance metrics. For example, what IC value means, what the Sharpe ratio represents, why high turnover rates make live trading impractical. The AI explains all these professional terms in plain language, letting you know the meaning behind each indicator. Most importantly is the improvement suggestions section. The AI won’t just tell you “it doesn’t work” and leave it at that - it will provide specific optimization directions based on validation results. For instance, changing the time period or using the factor in reverse. These suggestions are all derived from data analysis, not just random guesses.

So with each validation, even if the factor fails, you still learn something: how to write the code, why it failed, how to improve it. Over time, your understanding of quantitative trading will deepen.

Real Case: Small Amplitude Yesterday, Large Gains Today

Let me show you a specific validation case. I input an idea: “Small amplitude yesterday, large gains today,” let’s see how the AI handles it.

📊 Factor Evaluation Results

🎯 Overall Assessment

• 📈 Score: ⁴²⁄₁₀₀
• 🏆 Rating: C+
• 💡 Recommendation: Not recommended for use

🔍 Original Hypothesis Validation

• 💭 Hypothesis: Small amplitude yesterday predicts high probability of upward movement today
• ✅ Theoretical Validity: Theoretically somewhat reasonable - low volatility may be followed by trend breakouts or mean reversion
• 📊 Market Logic: Cryptocurrency markets show clear volatility clustering effects, but single-day amplitude predicting next-day movements has weak logic
• 💻 Code Accuracy: Implementation is accurate, correctly calculating yesterday’s amplitude and taking its negative value as the factor

📈 Performance Analysis Details

💰 Return Performance

• 📉 Annualized Return: -18.66% (expected positive returns, actual negative returns)
• 🎲 Win Rate: 56.4% (slightly above random)
• 📊 Cumulative Return: -24.57% (completely opposite to expectation)

⚠️ Risk Indicators

• 📉 Maximum Drawdown: 30.08% (poor risk control)
• 🌊 Volatility: 18.42% (high risk)
• ⚖️ Sharpe Ratio: -1.01 (severe losses after risk adjustment)

🔬 Statistical Test Results

📊 Predictive Ability (IC Analysis)

• 🎯 IC Mean: 0.063 (some predictive power, but weak)
• 📈 t-statistic: 2.93 (statistically significant > 2.0)
• 🔄 Rank IC: 0.053 (weak ranking prediction ability)
• 📉 Information Ratio (IR): 0.158 (weak predictive ability)

📏 Monotonicity Test

• 📊 Monotonicity Score: 0.083 (extremely weak)
• ✅ Monotonicity Rate: 3.49% (almost no monotonicity)
• 📈 Long-Short Spread: -0.0008 (long and short equally effective)

⏱️ Factor Stability

🔄 Persistence Analysis

• ⏰ Half-life: 1 day (factor signal decays extremely fast)
• 🔗 Autocorrelation: -0.093 (negative correlation, unstable signal)
• 📅 Recommended Rebalancing Frequency: Daily (requires high-frequency rebalancing)

💎 Market Cap Consistency

• 🏢 Large Cap IC: 0.040
• 🏪 Mid Cap IC: 0.037
• 🏬 Small Cap IC: 0.037
• ✅ Consistency: Good (similar performance across market caps)

💸 Trading Cost Analysis

🔄 Turnover Impact

• 📊 Average Daily Turnover: 41.95% (high-frequency trading)
• 💰 Cost Erosion: 10.26% annualized return
• 📉 Net Return: -28.92% (even worse after costs)

🎯 Factor Code Implementation

// Yesterday's Amplitude Factor Calculation
if (closes.length < 3 || highs.length < 3 || lows.length < 3) return null;

const yesterdayHigh = highs[highs.length - 2];
const yesterdayLow = lows[lows.length - 2];
const yesterdayClose = closes[closes.length - 2];
const yesterdayAmplitude = (yesterdayHigh - yesterdayLow) / Math.max(yesterdayClose, 0.0001);

return -yesterdayAmplitude; // Negative value: smaller amplitude = larger factor value

💡 Improvement Suggestions

• 1.🔄 Multi-day Amplitude: Try using multi-day amplitude averages or volatility clustering features instead of single-day amplitude
• 2.📊 Composite Factor: Combine with volume or price trends to build a composite factor that enhances signal strength
• 3.🔄 Reverse Logic: Consider reverse logic: mean reversion after high amplitude might be more effective

⚠️ Risk Warnings

• 1.📉 Negative Return Risk: Negative returns with high drawdowns - direct use will lead to continuous losses
• 2.💸 High Costs: High turnover rate causes trading costs to severely erode returns
• 3.📊 Instability: Extremely weak monotonicity, unstable relationship between factor and returns

🎯 Final Conclusion

The idea is reasonable but validation failed. Negative returns and high costs suggest abandoning it or testing the reverse logic.

What It Actually Feels Like to Use

This AI is pretty smart - it can understand whatever way you express yourself. For example, if you say “momentum effect,” it knows you’re talking about price trends continuing. If you say “mean reversion,” it knows prices will return to average levels. Even if you speak very colloquially, like saying “chase highs and sell lows” or “bottom fishing,” it can accurately understand. This means you don’t need to be a programmer - you just need to be able to clearly express your idea. Although most of the time it deals you a harsh blow, having incorrect ideas continuously rejected is also a small episode on the road to success.

What Happens When Validation Becomes Faster

When validation becomes very fast, your entire research habits change. Previously, you could validate at most two or three ideas per month, but now you can validate over ten ideas in a single day. Because you’re no longer afraid of failure and the validation cost is low, you dare to try all kinds of unusual ideas. Through large quantities of rapid validation, your understanding of the market deepens progressively. This is what they mean by quantitative change leading to qualitative change.

It’s Not a Silver Bullet

Of course, this tool isn’t omnipotent. AI’s comprehension ability is limited - ideas that are too complex might be misunderstood. Data coverage also has limitations; it can only validate phenomena present in historical data. And there’s the fact that historical effectiveness doesn’t guarantee future effectiveness - everyone understands this principle. This tool mainly helps you quickly screen ideas, eliminate obviously unreliable ones, and identify directions worth deeper research.

This Is Just the Beginning

Actually, the single-factor validation I’ve shared today is just the starting point for multi-factor models. In actual trading, the effectiveness of individual factors is often limited - what’s truly useful is multi-factor combinations. For example, combining momentum factors, volume factors, and volatility factors produces more stable results. If everyone is interested in this content, I’ll continue with videos on multi-factor validation, factor synthesis, and ultimately how to build live trading systems.

Conclusion

I think the greatest significance of this tool is that it gives every idea a chance to be validated. Previously, because it was too troublesome, many ideas were simply overlooked. Now that the barrier is lower, you can confidently and boldly validate all kinds of ideas. In this rapidly changing market, what’s most fearsome isn’t making mistakes - it’s missing opportunities. While you’re still hesitating about whether to validate an idea, others may have already validated ten ideas and found the useful one. Alright, that’s all for today’s sharing. Welcome everyone to the FMZ platform to try and experience more.

Appendix: Complete Source Code and Resources

Complete Source Code:

• FMZ Quantitative Platform: https://www.fmz.com/strategy/515798
• Can substitute different AI models and select validation cryptocurrencies

Risk Warning:

• This article is for technical learning only and does not constitute investment advice
• Cryptocurrency trading carries extremely high risks and may result in total loss of principal
• Be sure to conduct thorough testing before using real funds