### Install quant-trading-skill CLI via Script

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Provides a one-line installation script for the quant-trading-skill CLI tool using curl or wget. This script automatically downloads pre-built binaries or builds from source, supporting Linux, macOS, and Windows on amd64 and arm64 architectures.

```bash
# Install via curl (recommended)
curl -fsSL https://raw.githubusercontent.com/0xboji/quant-trading-skill/main/install.sh | bash

# Or via wget
wget -qO- https://raw.githubusercontent.com/0xboji/quant-trading-skill/main/install.sh | bash

# Installation creates:
# ~/.quant-trading-skill/
# 	├── quantpro           # Binary executable
# 	└── data/              # Knowledge base CSV files
#     ├── strategies.csv
#     ├── indicators.csv
#     ├── risk-management.csv
#     ├── data-sources.csv
#     └── anti-patterns.csv

# After installation, restart shell or run:
source ~/.bashrc  # or ~/.zshrc

# Verify installation
quantpro --version
# quantpro version 1.0.0
```

--------------------------------

### Install QuantPro CLI Tool

Source: https://github.com/0xboji/quant-trading-skill/blob/main/README.md

Installs the QuantPro command-line interface tool using a curl script. This command fetches the installation script from a GitHub raw URL and pipes it to bash for execution. Ensure you trust the source before running.

```bash
curl -fsSL https://raw.githubusercontent.com/0xboji/quant-trading-skill/main/install.sh | bash
```

--------------------------------

### Example: Identify Backtesting Mistakes

Source: https://github.com/0xboji/quant-trading-skill/blob/main/README.md

Illustrates searching for 'backtesting overfitting' within the 'anti-pattern' domain using QuantPro. The example output highlights the 'Overfitting on In-Sample Data' pitfall and provides 'Don't' and 'Do' recommendations.

```bash
$ quantpro search "backtesting overfitting" -d anti-pattern

====================================================================================================
QUERY: backtesting overfitting
====================================================================================================

Domain: anti-pattern
Found 2 results:

1. Overfitting on In-Sample Data
   Category: Strategy Design
   Severity: CRITICAL
   Don't: Optimize 10+ parameters, test only in-sample, maximize historical Sharpe
   Do: Use walk-forward analysis, out-of-sample testing, limit parameters (<5)
```

--------------------------------

### Example: Search Trading Strategies

Source: https://github.com/0xboji/quant-trading-skill/blob/main/README.md

Demonstrates searching for 'high frequency order flow' within the 'strategy' domain using the QuantPro CLI. The output includes details about matching strategies like Order Flow Imbalance (OFI) and Dual-Scale Hawkes Process.

```bash
$ quantpro search "high frequency order flow" -d strategy

====================================================================================================
QUERY: high frequency order flow
====================================================================================================

Domain: strategy
Found 3 results:

1. Order Flow Imbalance (OFI)
   Category: Microstructure
   Time Horizon: Intraday (1ms-60s)
   Complexity: Medium
   Best For: High-frequency futures, liquid crypto pairs, institutional execution

2. Dual-Scale Hawkes Process
   Category: Point Process
   Time Horizon: Ultra-short (100ms-10s)
   Complexity: High
   Best For: Detecting sweeps, momentum ignition, liquidity crises
```

--------------------------------

### Build and Run Quant Trading Skill CLI

Source: https://github.com/0xboji/quant-trading-skill/blob/main/README.md

Instructions to clone the repository, build the command-line interface (CLI) application, and verify its version. This process requires Go 1.21+ and Git.

```bash
git clone https://github.com/0xboji/quant-trading-skill.git
cd quant-trading-skill
go build -o quantpro ./cmd/quantpro
./quantpro --version
```

--------------------------------

### Build quant-trading-skill from Source using Go

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Instructions for manually building the quant-trading-skill project from source using Go 1.21+. Covers cloning the repository, building the binary, running locally, and cross-compiling for different platforms.

```bash
# Clone repository
git clone https://github.com/0xboji/quant-trading-skill.git
cd quant-trading-skill

# Build binary
go build -o quantpro ./cmd/quantpro

# Run locally
./quantpro --version
./quantpro search "order flow" -d strategy

# Cross-compile for different platforms
# Linux amd64
GOOS=linux GOARCH=amd64 go build -o quantpro-linux-amd64 ./cmd/quantpro

# macOS arm64 (Apple Silicon)
GOOS=darwin GOARCH=arm64 go build -o quantpro-darwin-arm64 ./cmd/quantpro

# Windows amd64
GOOS=windows GOARCH=amd64 go build -o quantpro-windows-amd64.exe ./cmd/quantpro
```

--------------------------------

### Initialize QuantPro Project

Source: https://github.com/0xboji/quant-trading-skill/blob/main/README.md

Initializes a new quantitative trading project with QuantPro. This command sets up the necessary directory structure (.agent/workflows and .shared/quant-trading-pro) and can integrate with an AI agent. The `--ai` flag specifies the AI model, and `--dir` allows custom target directory.

```bash
cd /path/to/your/quant-project
quantpro init --ai antigravity
```

```bash
# Custom target directory
quantpro init --ai custom-agent --dir /path/to/project
```

--------------------------------

### Initialize QuantPro Project Structure (Bash)

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Initializes the QuantPro skill within a project, creating necessary directories and populating them with the quantitative trading knowledge base and workflow documentation. It requires specifying an AI agent name using the `--ai` flag and optionally a project directory with `--dir`.

```bash
# Initialize with antigravity AI agent in current directory
quantpro init --ai antigravity

# Initialize with custom agent in specific directory
quantpro init --ai custom-agent --dir /path/to/project

# Creates the following structure:
# .agent/
# └── workflows/
#     └── use-quant-skill.md
# .shared/
# └── quant-trading-pro/
#     ├── data/           # 5 CSV files (122 entries)
#     │   ├── strategies.csv
#     │   ├── indicators.csv
#     │   ├── risk-management.csv
#     │   ├── data-sources.csv
#     │   └── anti-patterns.csv
#     └── SKILL.md       # Documentation
```

--------------------------------

### quantpro init - Initialize Project Structure

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Initializes the QuantPro skill in your project by creating necessary directories and populating them with the quantitative trading knowledge base and workflow documentation. Requires specifying an AI agent name.

```APIDOC
## quantpro init

### Description
Initializes QuantPro skill in your project by creating `.agent` and `.shared` directories with the quantitative trading knowledge base and workflow documentation. Requires specifying an AI agent name via the `--ai` flag.

### Method
CLI Command

### Endpoint
N/A

### Parameters
#### Command Flags
- **--ai** (string) - Required - The name of the AI agent.
- **--dir** (string) - Optional - The specific directory to initialize the project in. Defaults to the current directory.

### Request Example
```bash
# Initialize with antigravity AI agent in current directory
quantpro init --ai antigravity

# Initialize with custom agent in specific directory
quantpro init --ai custom-agent --dir /path/to/project
```

### Response
#### Success Response
Creates the following directory structure:
```
.agent/
└── workflows/
    └── use-quant-skill.md
.shared/
└── quant-trading-pro/
    ├── data/           # 5 CSV files (122 entries)
    │   ├── strategies.csv
    │   ├── indicators.csv
    │   ├── risk-management.csv
    │   ├── data-sources.csv
    │   └── anti-patterns.csv
    └── SKILL.md       # Documentation
```

#### Response Example
N/A (This is a CLI command that modifies the file system)
```

--------------------------------

### Cross-Compile Quant Trading Skill CLI for Different Platforms

Source: https://github.com/0xboji/quant-trading-skill/blob/main/README.md

Demonstrates how to cross-compile the quantpro CLI for various operating systems and architectures, including Linux amd64, macOS arm64, and Windows amd64. This is useful for distributing the application to different environments.

```bash
# Linux amd64
GOOS=linux GOARCH=amd64 go build -o quantpro-linux-amd64 ./cmd/quantpro

# macOS arm64 (M1/M2)
GOOS=darwin GOARCH=arm64 go build -o quantpro-darwin-arm64 ./cmd/quantpro

# Windows
GOOS=windows GOARCH=amd64 go build -o quantpro-windows-amd64.exe ./cmd/quantpro
```

--------------------------------

### Integrate Quant Trading Skill Search as a Go Package

Source: https://github.com/0xboji/quant-trading-skill/blob/main/README.md

Shows how to use the quant-trading-skill project's search functionality within another Go application. It imports the 'search' package and performs a search query against the local data directory, then iterates through the results.

```go
import "github.com/0xboji/quant-trading-skill/internal/search"

result, err := search.Search("./data", "order flow crypto", "", 3)
if err != nil {
    log.Fatal(err)
}

for _, r := range result.Results {
    fmt.Printf("%s: %s\n", r["Strategy Name"], r["Best For"])
}
```

--------------------------------

### Search Quant Trading Skills with `quantpro` CLI

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Command-line interface for searching various aspects of quantitative trading skills. Supports searching for position sizing methods, stop-loss strategies, risk controls, data sources, and common anti-patterns. Uses keywords and domain flags for targeted searches.

```bash
# Find position sizing methods
quantpro search "position sizing kelly criterion" -d risk

# Search stop-loss strategies
quantpro search "trailing stop atr volatility" -d risk

# Find portfolio risk controls
quantpro search "var cvar drawdown exposure limits" -d risk -n 5

# Find tick-level data sources
quantpro search "tick data order book l2" -d data

# Search alternative data
quantpro search "on-chain sentiment news nlp" -d data

# Find options data sources
quantpro search "options chain greeks implied volatility" -d data -n 5

# Find backtesting mistakes
quantpro search "overfitting look-ahead bias backtest" -d anti-pattern

# Search execution pitfalls
quantpro search "slippage fees transaction costs" -d anti-pattern

# Find data-related errors
quantpro search "survivorship bias data quality" -d anti-pattern -n 5
```

--------------------------------

### BM25 Search Algorithm Implementation in Go

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Demonstrates how to use the BM25 search algorithm for text indexing and scoring. It covers creating a BM25 engine, fitting documents, scoring queries, sorting results, and tokenization.

```go
package main

import (
    "fmt"
    "sort"

    "github.com/0xboji/quant-trading-skill/internal/bm25"
)

func main() {
    // Create BM25 engine with standard parameters
    // k1=1.5 (term frequency saturation)
    // b=0.75 (document length normalization)
    engine := bm25.New(1.5, 0.75)

    // Sample documents
    documents := []string{
        "order flow imbalance microstructure trading high frequency",
        "rsi macd bollinger bands technical indicators momentum",
        "position sizing kelly criterion risk management stop loss",
        "tick data order book level 2 market data feed",
    }

    // Build the index
    engine.Fit(documents)

    // Score all documents against query
    query := "order flow trading"
    results := engine.Score(query)

    // Sort by score descending
    sort.Slice(results, func(i, j int) bool {
        return results[i].Score > results[j].Score
    })

    // Print ranked results
    for _, r := range results {
        if r.Score > 0 {
            fmt.Printf("Doc %d: score=%.4f\n", r.Index, r.Score)
            fmt.Printf("  Content: %s\n", documents[r.Index])
        }
    }
    // Output:
    // Doc 0: score=2.3456
    //   Content: order flow imbalance microstructure trading high frequency

    // Tokenization example
    tokens := engine.Tokenize("Order Flow Trading!")
    fmt.Println(tokens) // ["order", "flow", "trading"]
}
```

--------------------------------

### Search Trading Strategies (Bash)

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Searches the 'strategy' domain for various trading strategies, including high-frequency, algorithmic, and market-making approaches. Users can specify keywords and use flags like `-d` for domain and `-n` for the number of results.

```bash
# Find HFT strategies
quantpro search "high frequency order flow" -d strategy

# Search for specific algorithm types
quantpro search "hawkes kalman microstructure" -d strategy

# Find market-making strategies
quantpro search "market making liquidity provision" -d strategy -n 5

# Example output fields:
# - Strategy Name: Order Flow Imbalance (OFI)
# - Category: Microstructure
# - Time Horizon: Intraday (1ms-60s)
# - Complexity: Medium
# - Best For: High-frequency futures, liquid crypto pairs
# - Capital Requirements: $10K-$100K+
# - Data Requirements: aggTrades (tick-level), price, quantity
```

--------------------------------

### Search QuantPro Knowledge Base (Bash)

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Performs BM25-powered searches across the QuantPro knowledge base, supporting automatic domain detection, domain-specific searches, and configurable result limits. It returns ranked results with relevant metadata.

```bash
# Auto-detect domain from query keywords
quantpro search "order flow crypto"

# Search specific domain with -d flag
quantpro search "stop loss kelly" -d risk

# Get more results with -n flag
quantpro search "rsi bollinger" -d indicator -n 5

# Use custom data directory
quantpro search "mean reversion crypto" --data-dir /custom/path/data

# Search anti-patterns to avoid common mistakes
quantpro search "backtesting overfitting" -d anti-pattern
```

--------------------------------

### Search.DetectDomain - Auto-Detect Query Domain

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Analyzes query keywords to automatically determine the most relevant domain. Uses keyword matching against domain-specific vocabularies.

```APIDOC
### search.DetectDomain - Auto-Detect Query Domain

#### Description
Analyzes query keywords to automatically determine the most relevant domain. Uses keyword matching against domain-specific vocabularies.

#### Method
`search.DetectDomain(query string) string`

#### Parameters

##### Path Parameters
- `query` (string) - Required - The search query string to analyze for domain detection.

#### Request Body
This function does not have a request body.

#### Response
##### Success Response (200)
- `string` - The detected domain name (e.g., "strategy", "indicator", "risk", "data", "anti-pattern"). Returns the default domain "strategy" if no keywords match.

### Request Example
```go
package main

import (
    "fmt"

    "github.com/0xboji/quant-trading-skill/internal/search"
)

func main() {
    // Domain detection based on keywords
    domain1 := search.DetectDomain("order flow microstructure")
    fmt.Println(domain1) // "strategy"

    domain2 := search.DetectDomain("rsi macd bollinger")
    fmt.Println(domain2) // "indicator"

    domain3 := search.DetectDomain("position sizing kelly")
    fmt.Println(domain3) // "risk"

    domain4 := search.DetectDomain("tick data order book")
    fmt.Println(domain4) // "data"

    domain5 := search.DetectDomain("overfitting look-ahead bias")
    fmt.Println(domain5) // "anti-pattern"

    // Default fallback when no keywords match
    domain6 := search.DetectDomain("something random")
    fmt.Println(domain6) // "strategy" (default)
}
```
```

--------------------------------

### Auto-Detect Query Domain with `search.DetectDomain` Go Function

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

The `search.DetectDomain` function automatically determines the most relevant domain for a given query by analyzing keywords against domain-specific vocabularies. It provides a fallback to 'strategy' if no keywords match. This is useful for routing queries to the correct search domain.

```go
package main

import (
    "fmt"

    "github.com/0xboji/quant-trading-skill/internal/search"
)

func main() {
    // Domain detection based on keywords
    domain1 := search.DetectDomain("order flow microstructure")
    fmt.Println(domain1) // "strategy"

    domain2 := search.DetectDomain("rsi macd bollinger")
    fmt.Println(domain2) // "indicator"

    domain3 := search.DetectDomain("position sizing kelly")
    fmt.Println(domain3) // "risk"

    domain4 := search.DetectDomain("tick data order book")
    fmt.Println(domain4) // "data"

    domain5 := search.DetectDomain("overfitting look-ahead bias")
    fmt.Println(domain5) // "anti-pattern"

    // Default fallback when no keywords match
    domain6 := search.DetectDomain("something random")
    fmt.Println(domain6) // "strategy" (default)
}
```

--------------------------------

### Search QuantPro Knowledge Base

Source: https://github.com/0xboji/quant-trading-skill/blob/main/README.md

Searches the QuantPro knowledge base for trading concepts. Supports auto-detection of the search domain or explicit domain specification using the `-d` flag. The `-n` flag controls the number of results returned.

```bash
# Auto-detect domain
quantpro search "order flow crypto"
```

```bash
# Search specific domain
quantpro search "stop loss kelly" -d risk
```

```bash
# Get more results
quantpro search "rsi bollinger" -d indicator -n 5
```

```bash
# Basic search (auto-detect domain)
quantpro search "mean reversion crypto"
```

```bash
# Domain-specific search
quantpro search "exponential moving average" -d indicator
```

```bash
# Get more results
quantpro search "position sizing" -d risk -n 5
```

```bash
# Custom data directory
quantpro search "query" --data-dir /path/to/data
```

--------------------------------

### Perform BM25 Search with `search.Search` Go Function

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

The `search.Search` function in Go performs BM25 search on a specified domain. It automatically detects the domain if not provided and returns a `Result` struct containing matched entries and their metadata. This function is useful for querying the quant-trading-skill knowledge base.

```go
package main

import (
    "fmt"
    "log"

    "github.com/0xboji/quant-trading-skill/internal/search"
)

func main() {
    // Search with auto-detected domain
    result, err := search.Search("./data", "order flow crypto", "", 3)
    if err != nil {
        log.Fatal(err)
    }

    fmt.Printf("Domain: %s\n", result.Domain)      // "strategy"
    fmt.Printf("Query: %s\n", result.Query)        // "order flow crypto"
    fmt.Printf("Found: %d results\n", result.Count) // 3

    for i, r := range result.Results {
        fmt.Printf("%d. %s\n", i+1, r["Strategy Name"])
        fmt.Printf("   Category: %s\n", r["Category"])
        fmt.Printf("   Best For: %s\n", r["Best For"])
    }

    // Search specific domain
    riskResult, err := search.Search("./data", "stop loss kelly", "risk", 5)
    if err != nil {
        log.Fatal(err)
    }

    for _, r := range riskResult.Results {
        fmt.Printf("Risk Control: %s\n", r["Risk Control"])
        fmt.Printf("Description: %s\n", r["Description"])
    }
}
```

--------------------------------

### Search Technical Indicators (Bash)

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Searches the 'indicator' domain for technical indicators, including momentum oscillators, volatility indicators, and volume-based indicators. The search can be refined by keywords and flags like `-d` for domain and `-n` for result count.

```bash
# Find momentum oscillators
quantpro search "rsi macd momentum oscillator" -d indicator

# Search volatility indicators
quantpro search "atr bollinger volatility bands" -d indicator

# Find volume-based indicators
quantpro search "volume profile obv vwap" -d indicator -n 5

# Example output fields:
# - Indicator Name: RSI (Relative Strength Index)
# - Category: Momentum Oscillator
# - Formula/Description: RSI = 100 - [100/(1 + RS)]
# - Parameters: Period (14), Overbought (70), Oversold (30)
```

--------------------------------

### Search.Search - Main Search Function

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Performs BM25 search on the specified domain with automatic domain detection. Returns a Result struct containing matched entries with all relevant metadata fields.

```APIDOC
## Go Package API

### search.Search - Main Search Function

#### Description
Performs BM25 search on the specified domain with automatic domain detection. Returns a Result struct containing matched entries with all relevant metadata fields.

#### Method
`search.Search(dataDir string, query string, domain string, limit int) (*search.Result, error)`

#### Parameters

##### Path Parameters
- `dataDir` (string) - Required - The directory containing the data files.
- `query` (string) - Required - The search query string.
- `domain` (string) - Optional - The specific domain to search within. If empty, the domain will be auto-detected.
- `limit` (int) - Required - The maximum number of results to return.

#### Request Body
This function does not have a request body.

#### Response
##### Success Response (200)
- `Result` (*search.Result) - An object containing the search results.
  - `Domain` (string) - The detected or specified search domain.
  - `Query` (string) - The original search query.
  - `Count` (int) - The number of results found.
  - `Results` ([]map[string]string) - A slice of maps, where each map represents a search result with its metadata.

##### Error Response
- `error` - An error object if the search fails.

### Request Example
```go
package main

import (
    "fmt"
    "log"

    "github.com/0xboji/quant-trading-skill/internal/search"
)

func main() {
    // Search with auto-detected domain
    result, err := search.Search("./data", "order flow crypto", "", 3)
    if err != nil {
        log.Fatal(err)
    }

    fmt.Printf("Domain: %s\n", result.Domain)      // "strategy"
    fmt.Printf("Query: %s\n", result.Query)        // "order flow crypto"
    fmt.Printf("Found: %d results\n", result.Count) // 3

    for i, r := range result.Results {
        fmt.Printf("%d. %s\n", i+1, r["Strategy Name"])
        fmt.Printf("   Category: %s\n", r["Category"])
        fmt.Printf("   Best For: %s\n", r["Best For"])
    }

    // Search specific domain
    riskResult, err := search.Search("./data", "stop loss kelly", "risk", 5)
    if err != nil {
        log.Fatal(err)
    }

    for _, r := range riskResult.Results {
        fmt.Printf("Risk Control: %s\n", r["Risk Control"])
        fmt.Printf("Description: %s\n", r["Description"])
    }
}
```
```

--------------------------------

### quantpro search - Search Knowledge Base

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Performs a BM25-powered search across the quantitative trading knowledge base. Supports automatic domain detection, domain-specific searches, and configurable result limits.

```APIDOC
## quantpro search

### Description
Performs BM25-powered search across the quantitative trading knowledge base. Supports automatic domain detection, domain-specific searches, and configurable result limits. Returns ranked results with relevant metadata fields.

### Method
CLI Command

### Endpoint
N/A

### Parameters
#### Command Flags
- **query** (string) - Required - The search query string.
- **-d** or **--domain** (string) - Optional - Specifies the domain to search within (e.g., 'strategy', 'indicator', 'risk', 'data-source', 'anti-pattern'). If not provided, the domain is auto-detected.
- **-n** or **--limit** (integer) - Optional - The maximum number of results to return. Defaults to 3.
- **--data-dir** (string) - Optional - Path to a custom data directory to use for searching.

### Request Example
```bash
# Auto-detect domain from query keywords
quantpro search "order flow crypto"

# Search specific domain with -d flag
quantpro search "stop loss kelly" -d risk

# Get more results with -n flag
quantpro search "rsi bollinger" -d indicator -n 5

# Use custom data directory
quantpro search "mean reversion crypto" --data-dir /custom/path/data

# Search anti-patterns to avoid common mistakes
quantpro search "backtesting overfitting" -d anti-pattern
```

### Response
#### Success Response
Returns ranked results with relevant metadata fields. The output format includes the query, domain, number of results found, and details for each result.

#### Response Example
```
====================================================================================================
QUERY: order flow crypto
====================================================================================================
Domain: strategy
Found 3 results:

1. Order Flow Imbalance (OFI)
   Category: Microstructure
   Time Horizon: Intraday (1ms-60s)
   Complexity: Medium
   Best For: High-frequency futures, liquid crypto pairs, institutional execution
```

#### Error Response
- **404 Not Found**: If the specified domain or data directory does not exist.
- **500 Internal Server Error**: For unexpected errors during the search process.
```

--------------------------------

### Search Domains

Source: https://context7.com/0xboji/quant-trading-skill/llms.txt

Details on the available domains for searching within the QuantPro knowledge base.

```APIDOC
## Available Search Domains

### strategy

#### Description
Search across high-frequency to long-term trading strategies including Order Flow Imbalance, Hawkes Process, Kalman Filter, Pairs Trading, Market Making, and ML/RL approaches. (24 entries)

#### Example Usage
```bash
# Find HFT strategies
quantpro search "high frequency order flow" -d strategy

# Search for specific algorithm types
quantpro search "hawkes kalman microstructure" -d strategy

# Find market-making strategies
quantpro search "market making liquidity provision" -d strategy -n 5
```

#### Example Output Fields
- Strategy Name: Order Flow Imbalance (OFI)
- Category: Microstructure
- Time Horizon: Intraday (1ms-60s)
- Complexity: Medium
- Best For: High-frequency futures, liquid crypto pairs
- Capital Requirements: $10K-$100K+
- Data Requirements: aggTrades (tick-level), price, quantity

### indicator

#### Description
Search technical indicators with formulas, parameters, and usage guidelines including EMA, RSI, MACD, ATR, Bollinger Bands, Volume Profile, and TCI. (23 entries)

#### Example Usage
```bash
# Find momentum oscillators
quantpro search "rsi macd momentum oscillator" -d indicator

# Search volatility indicators
quantpro search "atr bollinger volatility bands" -d indicator

# Find volume-based indicators
quantpro search "volume profile obv vwap" -d indicator -n 5
```

#### Example Output Fields
- Indicator Name: RSI (Relative Strength Index)
- Category: Momentum Oscillator
- Formula/Description: RSI = 100 - [100/(1 + RS)]
- Parameters: Period (14), Overbought (70), Oversold (30)

### risk-management

#### Description
Search risk management techniques including position sizing, stop-loss strategies, portfolio diversification, and drawdown control. (Entries vary)

### data-sources

#### Description
Information on various data sources suitable for quantitative trading, including APIs, data providers, and file formats. (Entries vary)

### anti-pattern

#### Description
Common pitfalls and anti-patterns to avoid in quantitative trading strategy development and backtesting, such as overfitting and look-ahead bias. (Entries vary)

#### Example Output Fields (for Overfitting)
- Anti-Pattern Name: Overfitting on In-Sample Data
- Category: Strategy Design
- Severity: CRITICAL
- Don't: Optimize 10+ parameters, test only in-sample, maximize historical Sharpe
- Do: Use walk-forward analysis, out-of-sample testing, limit parameters (<5)
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