> For the complete documentation index, see [llms.txt](https://backup1.infinityalgo.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://backup1.infinityalgo.com/backtest/backtest-version/settings-spreadsheet.md).

# 📋 Settings Spreadsheet

We don't provide a settings spreadsheet - and that's by design. This page explains why dynamic optimization is superior to static settings.

{% hint style="info" %}
**TL;DR:** Markets change constantly. Static settings that worked yesterday fail today. Use AI Optimization or develop your own adaptive approach through continuous testing.
{% endhint %}

<figure><img src="https://2387257950-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F5cf3dRpPzq1Qbyc8GksH%2Fuploads%2FDOYZoBeaHBkFF6U9d42d%2Fimage.png?alt=media&amp;token=f97508a0-3651-49eb-85c8-8ed47ce880bc" alt="AI optimization On"><figcaption></figcaption></figure>

***

### ❌ The Static Settings Trap

#### Why Spreadsheets Don't Work

{% columns %}
{% column width="50%" %}
**The Problem:**

* Markets evolve daily
* Volatility changes
* Correlations shift
* Liquidity varies
  {% endcolumn %}

{% column %}
**The False Promise:**

* "Best settings for BTCUSDT"
* "Copy my profitable settings"
* "Universal parameters"
* "Set and forget"
  {% endcolumn %}
  {% endcolumns %}

#### Real Market Example

{% hint style="warning" %}
**Case Study: Bitcoin 2024-2025**

**January 2024 Settings:**

* Sensitivity: 14
* Works great in trending market
* 70% win rate

**March 2024 Same Settings:**

* Market turns choppy
* 30% win rate
* Account down 20%

**Lesson:** What worked for 2 months failed completely when conditions changed.
{% endhint %}

***

### 🤖 The Solution: Dynamic Optimization

#### Static vs Dynamic Comparison

| Static Settings           | Dynamic AI              | Winner   |
| ------------------------- | ----------------------- | -------- |
| Fixed parameters          | Adapts every N bars     | **AI** ✅ |
| Manual updates            | Automatic optimization  | **AI** ✅ |
| Works until it doesn't    | Continuously evolves    | **AI** ✅ |
| One-size-fits-all         | Personalized to metrics | **AI** ✅ |
| Hope markets don't change | Responds to changes     | **AI** ✅ |

#### How AI Optimization Works

{% stepper %}
{% step %}

#### Continuous Testing

AI tests 1000s of combinations in background
{% endstep %}

{% step %}

#### Performance Scoring

Each combination scored by your chosen metric
{% endstep %}

{% step %}

#### Automatic Selection

Best performing settings applied automatically
{% endstep %}

{% step %}

#### Regular Updates

Process repeats every update cycle
{% endstep %}
{% endstepper %}

***

### 🎯 What Actually Matters

#### Success Factors (Ranked)

{% columns %}
{% column width="60%" %}
**1. Risk Management** (40%)

* Position sizing
* Stop losses
* Portfolio balance

**2. Market Context** (30%)

* Trend identification
* Support/resistance
* Volume analysis

**3. Psychology** (20%)

* Discipline
* Patience
* Emotional control

**4. Settings** (10%)

* Just 10% of success!
  {% endcolumn %}

{% column %}
{% hint style="success" %}
**Reality Check**

Perfect settings + Poor risk management = **Blown account**

Average settings + Good risk management = **Consistent profits**
{% endhint %}
{% endcolumn %}
{% endcolumns %}

***

### 📊 The "Best Settings" Myths

#### Myth #1: High Win Rate = Profitable

{% columns %}
{% column width="50%" %}
**90% Win Rate Strategy**

* Win: $10 (90% of time)
* Loss: $100 (10% of time)
* **Result: LOSING STRATEGY**
  {% endcolumn %}

{% column %}
**30% Win Rate Strategy**

* Win: $100 (30% of time)
* Loss: $20 (70% of time)
* **Result: PROFITABLE**
  {% endcolumn %}
  {% endcolumns %}

#### Myth #2: Copy Successful Traders

**Why it fails:**

* Different risk tolerance
* Different capital
* Different schedule
* Different psychology
* Different market conditions when they traded

#### Myth #3: Backtest = Future

{% hint style="danger" %}
**Overfitting Example:**

* Backtest: 500% profit
* Live trading: -50% loss

Why? Optimized perfectly for past data that won't repeat exactly.
{% endhint %}

***

### ✅ The Better Way

#### Develop Your Edge

{% tabs %}
{% tab title="Option 1: Use AI" %}
**Easiest approach:**

1. Enable AI Optimization
2. Select your metric
3. Let it adapt continuously
4. Focus on risk management

**Time required:** 5 minutes **Skill required:** Minimal **Effectiveness:** High
{% endtab %}

{% tab title="Option 2: Manual Adaptation" %}
**For control freaks:**

1. Test weekly
2. Document what works
3. Adjust for market conditions
4. Never "set and forget"

**Time required:** Hours weekly **Skill required:** High **Effectiveness:** Variable
{% endtab %}
{% endtabs %}

#### Focus Your Energy

**Instead of searching for settings:**

* ✅ Learn risk management
* ✅ Study market structure
* ✅ Practice with small size
* ✅ Build trading discipline
* ✅ Develop patience

**Your time allocation:**

* 60% Risk management
* 30% Market analysis
* 10% Settings/tools

***

### ❓ Common Questions

<details>

<summary><strong>But other indicators provide settings!</strong></summary>

Most indicators are static tools without AI. They NEED manual settings because they can't adapt.

Infinity Algo V3.0 has AI that makes static settings obsolete. It's like comparing a manual camera (need to set everything) to a modern smartphone (auto-adjusts for perfect shots).

</details>

<details>

<summary><strong>Can you just share what's working now?</strong></summary>

What's working NOW:

* **For me:** Won't work for you (different everything)
* **Today:** Won't work tomorrow (markets change)
* **On BTCUSDT:** Won't work on ETHUSDT (different behavior)

Instead: Use AI mode or test yourself weekly.

</details>

<details>

<summary><strong>I found profitable settings, should I share?</strong></summary>

Ask yourself:

* How long have they worked?
* In what market conditions?
* What's the sample size?
* What's the maximum drawdown?

If less than 100 trades across different conditions, it's luck, not edge.

</details>