### Install and Run MCP Server

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Commands to install the MCP support package and start the server.

```bash
# Install with MCP support
pip install ccxt-pandas[mcp]

# Run server
ccxt-pandas-mcp
```

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

### Install Dependencies with UV

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/CLAUDE.md

Use `uv sync` to install project dependencies. Additional flags can be used to install documentation or development dependencies.

```bash
uv sync
```

```bash
uv sync --extra docs
```

```bash
uv sync --group dev
```

```bash
uv sync --all-extras --all-groups
```

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

### Install MCP Server

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/introduction.md

Install the optional MCP server dependencies.

```bash
pip install ccxt-pandas[mcp]
```

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

### Install CCXT-Pandas

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/README.md

Command to install the library via pip.

```bash
pip install ccxt-pandas
```

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

### MCP Server Installation and Configuration

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/README.md

Commands and configuration formats for setting up the CCXT-Pandas MCP server.

```bash
pip install ccxt-pandas[mcp]
```

```json
{
  "accounts": {
    "binance": {
      "exchange": "binance",
      "api_key": "your_api_key",
      "secret": "your_secret",
      "sandbox_mode": true
    }
  },
  "read_only": true
}
```

```bash
export CCXT_MCP_ACCOUNT_BINANCE_EXCHANGE=binance
export CCXT_MCP_ACCOUNT_BINANCE_API_KEY=your_key
export CCXT_MCP_ACCOUNT_BINANCE_SECRET=your_secret
export CCXT_MCP_READ_ONLY=true
```

```bash
# Via CLI
ccxt-pandas-mcp

# Via uv
uv run ccxt-pandas-mcp
```

```json
{
  "mcpServers": {
    "ccxt-pandas": {
      "command": "uv",
      "args": ["run", "ccxt-pandas-mcp"],
      "env": {
        "CCXT_MCP_CONFIG": "/path/to/ccxt-mcp-config.json"
      }
    }
  }
}
```

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

### Install crypto-pandas Package

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/installation.md

Use this command to install the package on Python 3.11 to 3.13. Ensure pip is up to date.

```bash
pip install crypto-pandas
```

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

### Claude Code Skill Installation

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/README.md

Commands to install the CCXT-Pandas helper skill for Claude Code.

```bash
# Windows
cp .claude/skills/ccxt-pandas-helper.md %USERPROFILE%\.claude\skills\

# macOS/Linux
cp .claude/skills/ccxt-pandas-helper.md ~/.claude/skills/
```

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

### GET /fetch_balance

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/_autosummary/crypto_pandas.utils.ccxt_pandas_exchange_typed.md

Fetches the account balance and returns it as a pandas DataFrame.

```APIDOC
## GET /fetch_balance

### Description
Retrieves the current account balance from the exchange.

### Method
GET

### Parameters
#### Query Parameters
- **params** (dict) - Optional - Additional exchange-specific parameters.

### Response
#### Success Response (200)
- **balance** (DataFrame) - The account balance data.
```

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

### fetch_tickers - Fetch All Tickers

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Retrieves current ticker information for all or specified symbols. Useful for getting a quick overview of market prices.

```APIDOC
## GET /api/v1/tickers

### Description
Retrieves current ticker information for all available trading symbols or a specified subset of symbols.

### Method
GET

### Endpoint
/api/v1/tickers

### Parameters
#### Query Parameters
- **symbol** (string or list) - Optional - A single symbol or a list of symbols to fetch tickers for (e.g., "BTC/USDT" or ["BTC/USDT", "ETH/USDT"]). If not provided, tickers for all symbols are fetched.

### Request Example
```python
# Fetch all tickers
tickers = exchange.fetch_tickers()

# Fetch specific symbols
tickers_subset = exchange.fetch_tickers(symbol=["BTC/USDT", "ETH/USDT"])
```

### Response
#### Success Response (200)
- **symbol** (string) - Trading symbol
- **last** (float) - Last traded price
- **bid** (float) - Best bid price
- **ask** (float) - Best ask price
- **baseVolume** (float) - Volume of the base currency traded in the last 24 hours
- **percentage** (float) - Percentage change in price over the last 24 hours

#### Response Example
```json
{
  "symbol": "BTC/USDT",
  "last": 42150.00,
  "bid": 42149.50,
  "ask": 42150.50,
  "baseVolume": 12345.678901,
  "percentage": 2.35
}
```
```

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

### Place Limit Maker and Queue Orders

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/examples.md

Demonstrates placing LIMIT_MAKER and QUEUE orders in a sandbox environment. These order types are suitable for market making strategies.

```python
import ccxt_pandas as cpd

# Load exchange in sandbox mode
exchange = cpd.load_exchange('binance', sandbox=True)

# Define order parameters
symbol = 'BTC/USDT'
side = 'buy'
amount = 0.01
price = 30000

# Place LIMIT_MAKER order
order_maker = exchange.create_order(symbol, 'limit', side, amount, price, {'type': 'LIMIT_MAKER'})
print('LIMIT_MAKER Order:', order_maker)

# Place QUEUE order (if supported)
# order_queue = exchange.create_order(symbol, 'limit', side, amount, price, {'type': 'QUEUE'})
# print('QUEUE Order:', order_queue)

```

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

### Initialize and Use CCXTPandasExchange

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/README.md

Demonstrates initializing a synchronous exchange object and fetching market data as DataFrames.

```python
# Initialize a CCXTPandasExchange object
exchange = ccxt.binance(dict(apiKey="your_api_key_here", secret="your_secret_here"))
exchange = CCXTPandasExchange(exchange=exchange)

# OHLCV
ohlcv = exchange.fetch_ohlcv("BTC/USDT", timeframe="1m", limit=100)      # -> DataFrame
# Trades
trades = exchange.fetch_trades("BTC/USDT", limit=1000)                   # -> DataFrame
# Orderbook
ob = exchange.fetch_order_book("BTC/USDT", limit=50)                 # -> DataFrame
# Tickers
tick = exchange.fetch_tickers()                               # -> DataFrame

# Fetch open orders from an exchange
open_orders = exchange.fetch_open_orders(symbol="BTC/USDT")

# Halve the amount and edit orders
open_orders["amount"] /= 2
response = exchange.edit_orders(open_orders)

# Display the transformed orders dataframe
print(response)
```

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

### Run MCP Server

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/introduction.md

Execute the MCP server using CLI or uv.

```bash
# Via CLI
ccxt-pandas-mcp

# Via uv
uv run ccxt-pandas-mcp
```

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

### GET has_method

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/crypto_pandas.md

Checks if a specific CCXT method is available for dynamic resolution.

```APIDOC
## GET has_method

### Description
Determines if the processor supports a specific CCXT method name.

### Method
GET

### Endpoint
has_method(method_name: str)

### Parameters
#### Path Parameters
- **method_name** (str) - Required - The name of the CCXT method to check.

### Response
#### Success Response (200)
- **result** (bool) - Returns true if the method exists, false otherwise.
```

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

### Configure MCP Server

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/introduction.md

Define account credentials and settings via JSON or environment variables.

```json
{
  "accounts": {
    "binance": {
      "exchange": "binance",
      "api_key": "your_api_key",
      "secret": "your_secret",
      "sandbox_mode": true
    }
  },
  "read_only": true
}
```

```bash
export CCXT_MCP_ACCOUNT_BINANCE_EXCHANGE=binance
export CCXT_MCP_ACCOUNT_BINANCE_API_KEY=your_key
export CCXT_MCP_ACCOUNT_BINANCE_SECRET=your_secret
export CCXT_MCP_READ_ONLY=true
```

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

### Initialize CCXTPandasExchange

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/introduction.md

Wraps a standard CCXT exchange instance with CCXTPandasExchange for DataFrame-based output.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange

# Initialize a CCXTPandasExchange object
exchange = ccxt.binance(dict(apiKey="your_api_key_here", secret="your_secret_here"))
exchange = CCXTPandasExchange(exchange=exchange)
```

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

### GET load_cached_markets

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/crypto_pandas.ccxt.async_ccxt_pandas_exchange.md

Asynchronously loads and caches market data from the exchange, returning it as a pandas DataFrame.

```APIDOC
## GET load_cached_markets

### Description
Loads and caches markets data asynchronously. This method allows for optional parameters to customize the data retrieval process.

### Method
GET

### Endpoint
load_cached_markets

### Parameters
#### Query Parameters
- **params** (dict) - Optional - Custom parameters for the market data request.

### Response
#### Success Response (200)
- **data** (pd.DataFrame) - A pandas DataFrame containing the cached market information.
```

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

### GET load_cached_markets

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/crypto_pandas.ccxt.ccxt_pandas_exchange.md

Loads market data from the exchange and returns it as a Pandas DataFrame, utilizing a caching mechanism to improve performance.

```APIDOC
## GET load_cached_markets

### Description
Loads market data from the exchange and caches it for a specified duration. Returns the market data as a Pandas DataFrame.

### Method
GET

### Endpoint
load_cached_markets(params={})

### Parameters
#### Query Parameters
- **params** (dict) - Optional - Additional parameters for the exchange's market-loading function.

### Response
#### Success Response (200)
- **data** (pd.DataFrame) - A DataFrame containing the market data.
```

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

### Fetch Spot, Future, and Swap Prices and Volumes

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/examples.md

Analyze BTC spread and volume across different contract types. This script helps in understanding market dynamics for various trading instruments.

```python
import ccxt_pandas as cpd

# Load all exchanges
# exchanges = cpd.load_exchanges()

# Load specific exchanges
exchanges = cpd.load_exchanges(['binance', 'binanceusdm', 'binancecoinm'])

# Fetch OHLCV data for BTC/USDT
df = cpd.fetch_ohlcv(exchanges, 'BTC/USDT', timeframe='1h', limit=1000)

# Calculate spread and volume
df['spread'] = df['ask'] - df['bid']
df['volume'] = df['quoteVolume']

# Display results
print(df.head())

# Plot BTC spread and volume
df[['spread', 'volume']].plot()

```

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

### AsyncCCXTPandasExchange Configuration

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/crypto_pandas.ccxt.async_ccxt_pandas_exchange.md

Configuration parameters for initializing the AsyncCCXTPandasExchange instance.

```APIDOC
## Configuration Parameters

### Description
Initialization parameters for the AsyncCCXTPandasExchange class to manage exchange behavior, concurrency, and data validation.

### Parameters
- **exchange** (ccxt.Exchange) - Required - The ccxt exchange instance.
- **max_order_cost** (float) - Optional - Maximum allowable cost for a single order (default: 10000).
- **max_number_of_orders** (int) - Optional - Maximum number of orders for bulk processing (default: 5).
- **markets_cache_time** (int) - Optional - Cache duration in seconds (default: 3600).
- **semaphore_value** (int) - Optional - Concurrency limit for requests (default: 1000).
- **cost_out_of_range** (str) - Optional - Behavior for cost violations ('warn' or 'clip').
- **amount_out_of_range** (str) - Optional - Behavior for volume violations ('warn' or 'clip').
- **price_out_of_range** (str) - Optional - Behavior for price violations ('warn' or 'clip').
```

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

### Fetch Option

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/_autosummary/crypto_pandas.utils.ccxt_pandas_exchange_typed.md

Retrieves option details for a specific symbol and returns it as a dictionary.

```APIDOC
## POST /fetch_option

### Description
Retrieves option details for a specific symbol and returns it as a dictionary.

### Method
POST

### Endpoint
/fetch_option

### Parameters
#### Query Parameters
- **symbol** (str) - Required - The symbol for which to fetch option details.
- **params** (dict) - Optional - Additional parameters for the request.

### Response
#### Success Response (200)
- **dict** - A dictionary containing the option details.
```

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

### Get directional sign for order book sides

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/ccxt_pandas/calculations/README.md

Returns +1 for asks/sell and -1 for bids/buy based on the 'side' column in a DataFrame.

```python
import pandas as pd
import ccxt_pandas as cpd

orderbook = pd.DataFrame({'side': ['bids', 'asks', 'bids']})
signs = cpd.side_sign(orderbook)
print(signs)
# 0   -1
# 1    1
# 2   -1
```

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

### Fetch Account Balances

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Retrieves account balances including free, used, and total amounts for each asset.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange

exchange = ccxt.binance({
    "apiKey": "your_api_key",
    "secret": "your_secret"
})
pandas_exchange = CCXTPandasExchange(exchange=exchange)

# Fetch balance
balance = pandas_exchange.fetch_balance()
print(balance[balance["total"] > 0][["code", "free", "used", "total"]])
#   code         free       used        total
# 0  BTC   1.23456789  0.1234567   1.35802459
# 1 USDT  10000.00000  500.00000  10500.00000
```

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

### Git Branching Workflow

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/README.md

Standard commands for contributing to the repository.

```bash
git checkout -b my-new-feature
git commit -am 'Add some feature'
git push origin my-new-feature
```

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

### Initialize CCXTPandasExchange

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/README.md

Basic boilerplate for wrapping a CCXT exchange object with CCXTPandasExchange.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange
```

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

### Fetch Option Chain

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/_autosummary/crypto_pandas.utils.ccxt_pandas_exchange_typed.md

Retrieves the option chain for a given code and returns it as a dictionary.

```APIDOC
## POST /fetch_option_chain

### Description
Retrieves the option chain for a given code and returns it as a dictionary.

### Method
POST

### Endpoint
/fetch_option_chain

### Parameters
#### Query Parameters
- **code** (str) - Required - The code for which to fetch the option chain.
- **params** (dict) - Optional - Additional parameters for the request.

### Response
#### Success Response (200)
- **dict** - A dictionary containing the option chain data.
```

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

### Create Single Limit Order by Amount

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Creates a single limit order for a specified amount and price. Ensure sandbox mode is set for testing. Requires ccxt and ccxt_pandas.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange

exchange = ccxt.binance({
    "apiKey": "your_api_key",
    "secret": "your_secret"
})
exchange.set_sandbox_mode(True)
pandas_exchange = CCXTPandasExchange(exchange=exchange)

# Create limit order by amount
order = pandas_exchange.create_order(
    symbol="BTC/USDT",
    type="limit",
    side="buy",
    amount=0.001,
    price=40000.0
)
print(order[["id", "symbol", "type", "side", "price", "amount", "status"]])
```

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

### Initialize Synchronous CCXTPandasExchange

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Wrap a CCXT exchange instance with CCXTPandasExchange for DataFrame output. Configure options like dropna_fields, max_order_cost, max_number_of_orders, and error handling.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange

# Initialize with any CCXT exchange
exchange = ccxt.binance({
    "apiKey": "your_api_key",
    "secret": "your_secret"
})
exchange.set_sandbox_mode(True)  # Use sandbox for testing

# Wrap with CCXTPandasExchange
pandas_exchange = CCXTPandasExchange(
    exchange=exchange,
    dropna_fields=True,           # Remove empty columns
    max_order_cost=10_000,        # Max order value limit
    max_number_of_orders=1_000,   # Batch order limit
    errors="raise"                # Error handling: "raise", "warn", "ignore"
)

# Load available markets
markets = pandas_exchange.load_markets()
print(markets[["symbol", "base", "quote", "type"]].head())
#        symbol base quote    type
# 0   BTC/USDT  BTC  USDT    spot
# 1   ETH/USDT  ETH  USDT    spot
# 2  BTC/USDT:USDT  BTC  USDT  swap

```

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

### fetch_balance - Fetch Account Balances

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Retrieves account balances, including free, used, and total amounts for each currency.

```APIDOC
## GET /api/v1/balance

### Description
Retrieves the current balances of your exchange account, detailing the free, used, and total amounts for each currency.

### Method
GET

### Endpoint
/api/v1/balance

### Parameters
This endpoint does not accept any parameters.

### Request Example
```python
# Fetch balance
balance = pandas_exchange.fetch_balance()
print(balance[balance["total"] > 0][["code", "free", "used", "total"]])
```

### Response
#### Success Response (200)
- **code** (string) - The currency code (e.g., "BTC", "USDT")
- **free** (float) - The amount of currency available for trading
- **used** (float) - The amount of currency currently used in open orders
- **total** (float) - The total amount of currency in the account

#### Response Example
```json
{
  "code": "BTC",
  "free": 1.23456789,
  "used": 0.1234567,
  "total": 1.35802459
}
```
```

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

### Configuration Options

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/_autosummary/crypto_pandas.md

Configuration parameters for customizing CCXT-Pandas behavior.

```APIDOC
## Configuration Options

This section details the configurable parameters for the CCXT-Pandas integration.

### `dropna_fields`

* **Description:** Determines whether empty (NaN) columns are removed from DataFrame outputs.
* **Type:** `bool`
* **Default:** `True`

### `attach_trades_to_orders`

* **Description:** Determines whether trades are attached to orders when processing orders.
* **Type:** `bool`
* **Default:** `False`

### `max_order_cost`

* **Description:** Maximum cost value for any single order.
* **Type:** `float`
* **Default:** `10000`

### `max_number_of_orders`

* **Description:** Maximum number of bulk orders allowed.
* **Type:** `int`
* **Default:** `5`

### `markets_cache_time`

* **Description:** Cache duration (in seconds) for markets data.
* **Type:** `int`
* **Default:** `3600`

### `cost_out_of_range`

* **Description:** Defines behavior when cost exceeds acceptable ranges.
* **Options:** `"warn"` (Logs a warning while removing the order), `"clip"` (Clips or limits the volume to valid ranges).
* **Type:** `str`
* **Default:** `"warn"`

### `amount_out_of_range`

* **Description:** Defines behavior when volume exceeds acceptable ranges.
* **Options:** `"warn"` (Logs a warning while removing the order), `"clip"` (Clips or limits the volume to valid ranges).
* **Type:** `str`
* **Default:** `"warn"`

### `price_out_of_range`

* **Description:** Defines behavior when price exceeds allowable ranges.
* **Options:** `"warn"` (Logs a warning while removing the order), `"clip"` (Adjusts the price to fit within predefined limits).
* **Type:** `str`
* **Default:** `"warn"`

### `_ccxt_processor`

* **Description:** A helper class to process CCXT responses and provide consistent output.
* **Type:** `BaseProcessor`

### `__getattr__(method_name: str)`

* **Description:** Overridden to enable dynamic method resolution for CCXT methods, with transformations applied to handle inputs and outputs as Pandas DataFrames.
* **Type:** `str`

### `account_name`

* **Description:** The name of the account.
* **Type:** `str | None`
* **Default:** `None`

### `exchange`

* **Description:** The CCXT exchange instance.
* **Type:** `Exchange`

### `exchange_name`

* **Description:** The name of the exchange.
* **Type:** `str | None`
* **Default:** `None`

### `has_method(method_name: str) -> bool`

* **Description:** Checks if the exchange supports a given method.
* **Parameters:**
  * **method_name** (`str`) - The name of the method to check.
* **Returns:** `bool` - True if the method is supported, False otherwise.
```

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

### Initialize Asynchronous AsyncCCXTPandasExchange

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Use AsyncCCXTPandasExchange with CCXT Pro for concurrent operations. Configure concurrency limits using semaphore_value.

```python
import asyncio
import ccxt.pro as ccxt_pro
from ccxt_pandas import AsyncCCXTPandasExchange

exchange = ccxt_pro.binance()
pandas_exchange = AsyncCCXTPandasExchange(
    exchange=exchange,
    semaphore_value=1000  # Concurrency limit
)

async def main():
    # Load markets
    markets = await pandas_exchange.load_markets()
    symbols = markets["symbol"].head(100).tolist()

    # Fetch OHLCV for multiple symbols concurrently
    tasks = [
        pandas_exchange.fetch_ohlcv(symbol=s, timeframe="1h", limit=100)
        for s in symbols
    ]
    results = await asyncio.gather(*tasks, return_exceptions=True)

    # Combine successful results
    import pandas as pd
    ohlcv_data = pd.concat([r for r in results if isinstance(r, pd.DataFrame)])
    print(f"Fetched {len(ohlcv_data)} candles across {len(symbols)} symbols")

    await exchange.close()

asyncio.run(main())

```

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

### Calculate Net Delta Across Spot and Derivatives

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/examples.md

Compute the net delta exposure across both spot and derivative positions in a sandbox environment. This helps in managing overall portfolio risk.

```python
import ccxt_pandas as cpd

# Load exchange in sandbox mode
exchange = cpd.load_exchange('binance', sandbox=True)

# Fetch positions
positions = exchange.fetch_positions()

# Calculate net delta
net_delta = cpd.calculate_delta_position(positions)

# Display results
print('Net Delta:', net_delta)

```

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

### Stream Live Liquidation Events via WebSocket

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/examples.md

Subscribe to live liquidation events using WebSockets. This script is essential for monitoring and reacting to high-volatility events in the derivatives market.

```python
import ccxt_pandas as cpd

# Load exchange
exchange = cpd.load_exchange('binanceusdm')

# Define a callback function for liquidation events
def on_liquidation(data):
    print('Liquidation Event:', data)

# Subscribe to liquidations channel
exchange.websockets_listen_liquidations(on_liquidation)

```

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

### Configure MCP Server

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

JSON configuration for defining exchange accounts and access restrictions.

```json
{
  "accounts": {
    "binance": {
      "exchange": "binance",
      "api_key": "your_api_key",
      "secret": "your_secret",
      "sandbox_mode": true
    },
    "bybit": {
      "exchange": "bybit",
      "api_key": "your_api_key",
      "secret": "your_secret",
      "sandbox_mode": true
    }
  },
  "read_only": true,
  "allowed_symbols": ["BTC/USDT", "ETH/USDT"],
  "blocked_symbols": []
}
```

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

### Load Markets from All Exchanges Asynchronously

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/examples.md

Asynchronously load market data from all available exchanges. This provides a comprehensive overview of available trading pairs and their properties.

```python
import ccxt_pandas as cpd

# Load all exchanges asynchronously
all_exchanges = cpd.load_symbols_all_exchanges_async()

# Access market data for a specific exchange
# print(all_exchanges['binance'].head())

# Example: Get all symbols from Binance
binance_symbols = all_exchanges.get('binance', cpd.pd.DataFrame())['symbol'].tolist()
print('Binance Symbols:', binance_symbols[:10])

```

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

### Create Single Market Order by Cost

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Creates a single market order by specifying the total cost, which will be executed at the current market price. Requires ccxt and ccxt_pandas.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange

exchange = ccxt.binance({
    "apiKey": "your_api_key",
    "secret": "your_secret"
})
exchange.set_sandbox_mode(True)
pandas_exchange = CCXTPandasExchange(exchange=exchange)

# Create market order
market_order = pandas_exchange.create_order(
    symbol="BTC/USDT",
    type="market",
    side="buy",
    cost=50.0  # $50 worth at market price
)
```

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

### create_order

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Places a single order with automatic precision handling.

```APIDOC
## create_order

### Description
Creates a single order. Supports creation by amount or by cost.

### Parameters
#### Request Body
- **symbol** (string) - Required - The trading pair symbol
- **type** (string) - Required - Order type (e.g., "limit", "market")
- **side** (string) - Required - Order side ("buy" or "sell")
- **amount** (float) - Optional - Order amount
- **price** (float) - Optional - Order price
- **cost** (float) - Optional - Total cost for the order
```

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

### POST /create_order

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/_autosummary/crypto_pandas.utils.ccxt_pandas_exchange_typed.md

Creates a new order on the exchange.

```APIDOC
## POST /create_order

### Description
Creates a new order on the exchange and returns the order details as a dictionary.

### Method
POST

### Parameters
#### Request Body
- **symbol** (str) - Required - The trading symbol.
- **type** (Literal['limit', 'market']) - Required - The order type.
- **side** (Literal['buy', 'sell']) - Required - The order side.
- **amount** (float) - Required - The order amount.
- **price** (None | str | float | int | Decimal) - Optional - The order price.
- **params** (dict) - Optional - Additional exchange-specific parameters.

### Response
#### Success Response (200)
- **order** (dict) - The created order details.
```

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

### Integrate with Claude Desktop

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/introduction.md

Add the MCP server configuration to your Claude Desktop or Claude Code client.

```json
{
  "mcpServers": {
    "ccxt-pandas": {
      "command": "uv",
      "args": ["run", "ccxt-pandas-mcp"],
      "env": {
        "CCXT_MCP_CONFIG": "/path/to/ccxt-mcp-config.json"
      }
    }
  }
}
```

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

### Fetch Open Positions

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Retrieves open positions for derivatives trading, supporting filtering by symbol.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange

exchange = ccxt.binance({
    "apiKey": "your_api_key",
    "secret": "your_secret"
})
pandas_exchange = CCXTPandasExchange(exchange=exchange)

# Fetch all positions
positions = pandas_exchange.fetch_positions()
print(positions[["symbol", "side", "contracts", "entryPrice", "unrealizedPnl"]])
#           symbol  side  contracts  entryPrice  unrealizedPnl
# 0  BTC/USDT:USDT  long       0.50     42000.0         150.00
# 1  ETH/USDT:USDT short       2.00      2200.0         -50.00

# Fetch positions for specific symbols
positions = pandas_exchange.fetch_positions(symbol=["BTC/USDT:USDT", "ETH/USDT:USDT"])
```

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

### Place and Edit Orders via WebSocket

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/examples.md

Send orders and edit existing orders through WebSocket connections in a sandbox environment. This enables real-time order management with lower latency.

```python
import ccxt_pandas as cpd

# Load exchange in sandbox mode
exchange = cpd.load_exchange('binance', sandbox=True)

# Define order parameters
symbol = 'BTC/USDT'
side = 'buy'
amount = 0.01
price = 30000

# Define callback for order events
def on_order_update(order):
    print('Order Update:', order)

# Send a new order via WebSocket
order_id = exchange.send_order_ws(symbol, 'limit', side, amount, price, callback=on_order_update)

# Edit an existing order via WebSocket (example)
# new_price = 30001
# updated_order = exchange.edit_order_ws(order_id, symbol, 'limit', side, amount, new_price, callback=on_order_update)

```

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

### Fetch Deposit and Withdrawal History

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/examples.md

Retrieve the history of deposits and withdrawals from a sandbox account. This is useful for auditing and tracking fund movements.

```python
import ccxt_pandas as cpd

# Load exchange in sandbox mode
exchange = cpd.load_exchange('binance', sandbox=True)

# Fetch deposit history
deposits = exchange.fetch_deposits()
print('Deposits:', deposits)

# Fetch withdrawal history
withdrawals = exchange.fetch_withdrawals()
print('Withdrawals:', withdrawals)

```

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

### Account and Balance API

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/_autosummary/crypto_pandas.utils.async_ccxt_pandas_exchange_typed.md

Methods for fetching account information and balances.

```APIDOC
## GET /api/v1/accounts

### Description
Fetches account information.

### Method
GET

### Endpoint
/api/v1/accounts

### Parameters
#### Query Parameters
- **params** (dict) - Optional - Additional parameters for the exchange.

### Response
#### Success Response (200)
- **DataFrame** (pd.DataFrame) - A DataFrame containing account information.

#### Response Example
```json
{
  "example": "[DataFrame representing account information]"
}
```

## GET /api/v1/balance

### Description
Fetches the current account balance.

### Method
GET

### Endpoint
/api/v1/balance

### Parameters
#### Query Parameters
- **params** (dict) - Optional - Additional parameters for the exchange.

### Response
#### Success Response (200)
- **DataFrame** (pd.DataFrame) - A DataFrame containing balance information.

#### Response Example
```json
{
  "example": "[DataFrame representing balance information]"
}
```
```

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

### fetch_funding_rates - Fetch Perpetual Funding Rates

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Retrieves current funding rates for perpetual contracts. Also provides a method to fetch historical funding rates.

```APIDOC
## GET /api/v1/funding_rates

### Description
Retrieves the current funding rates for perpetual futures contracts. A separate method is available to fetch historical funding rates.

### Method
GET

### Endpoint
/api/v1/funding_rates

### Parameters
This endpoint does not accept any parameters for fetching current rates.

### Request Example
```python
# Fetch current funding rates
funding_rates = exchange.fetch_funding_rates()
```

### Response
#### Success Response (200)
- **symbol** (string) - Trading symbol for the perpetual contract
- **fundingRate** (float) - The current funding rate
- **fundingTimestamp** (string) - The timestamp when the funding rate is effective

#### Response Example
```json
{
  "symbol": "BTC/USDT:USDT",
  "fundingRate": 0.000100,
  "fundingTimestamp": "2024-01-15 16:00:00+00:00"
}
```

## GET /api/v1/funding_rate_history

### Description
Retrieves historical funding rates for a specified perpetual contract symbol.

### Method
GET

### Endpoint
/api/v1/funding_rate_history

### Parameters
#### Query Parameters
- **symbol** (string) - Required - The trading symbol for the perpetual contract (e.g., "BTC/USDT:USDT")
- **limit** (integer) - Optional - The maximum number of historical funding rates to retrieve.

### Request Example
```python
# Fetch historical funding rate
funding_history = exchange.fetch_funding_rate_history(
    symbol="BTC/USDT:USDT",
    limit=1000
)
```

### Response
#### Success Response (200)
- **symbol** (string) - Trading symbol for the perpetual contract
- **fundingRate** (float) - The funding rate at the specified time
- **fundingTimestamp** (string) - The timestamp of the funding rate

#### Response Example
```json
{
  "symbol": "BTC/USDT:USDT",
  "fundingRate": 0.000100,
  "fundingTimestamp": "2024-01-15 16:00:00+00:00"
}
```
```

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

### Create Single Limit Order by Cost

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Creates a single limit order by specifying the total cost, automatically calculating the order amount. Requires ccxt and ccxt_pandas.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange

exchange = ccxt.binance({
    "apiKey": "your_api_key",
    "secret": "your_secret"
})
exchange.set_sandbox_mode(True)
pandas_exchange = CCXTPandasExchange(exchange=exchange)

# Create order by cost (automatically calculates amount)
order = pandas_exchange.create_order(
    symbol="BTC/USDT",
    type="limit",
    side="buy",
    cost=100.0,  # $100 worth
    price=40000.0
)
```

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

### Fetch Perpetual Funding Rates

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Retrieves current funding rates for perpetual contracts and historical funding rate data.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange

exchange = CCXTPandasExchange(exchange=ccxt.binance())

# Fetch current funding rates
funding_rates = exchange.fetch_funding_rates()
print(funding_rates[["symbol", "fundingRate", "fundingTimestamp"]].head())
#           symbol  fundingRate           fundingTimestamp
# 0  BTC/USDT:USDT     0.000100  2024-01-15 16:00:00+00:00
# 1  ETH/USDT:USDT     0.000075  2024-01-15 16:00:00+00:00

# Fetch historical funding rate
funding_history = exchange.fetch_funding_rate_history(
    symbol="BTC/USDT:USDT",
    limit=1000
)
```

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

### Fetch Best Bid/Ask Prices

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Retrieves the best bid and ask prices for all symbols.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange

exchange = CCXTPandasExchange(exchange=ccxt.binance())

# Fetch best bid/ask
bids_asks = exchange.fetch_bids_asks()
print(bids_asks[["symbol", "bid", "ask", "bidVolume", "askVolume"]].head())
#      symbol       bid       ask  bidVolume  askVolume
# 0  BTC/USDT  42149.50  42150.50   1.234567   0.987654
# 1  ETH/USDT   2249.80   2250.20   5.432100   3.210987
```

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

### Run Pytest Tests

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/CLAUDE.md

Execute tests using `uv run pytest`. This includes running all tests, a single test file, or a specific test case. Ensure API keys are configured in a `.env` file.

```bash
uv run pytest tests/
```

```bash
uv run pytest tests/test_sync.py::test_fetch_ohlcv -s
```

```bash
uv run python tests/test_async.py
```

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

### Fetch My Liquidations

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/_autosummary/crypto_pandas.utils.ccxt_pandas_exchange_typed.md

Retrieves liquidation information for the current user and returns it as a Pandas DataFrame.

```APIDOC
## POST /fetch_my_liquidations

### Description
Retrieves liquidation information for the current user and returns it as a Pandas DataFrame.

### Method
POST

### Endpoint
/fetch_my_liquidations

### Parameters
#### Query Parameters
- **symbol** (str | None) - Optional - The symbol to filter liquidations by.
- **since** (int | Timestamp | dict | str | None) - Optional - The starting timestamp for the liquidation entries.
- **limit** (int | None) - Optional - The maximum number of liquidation entries to retrieve.
- **params** (dict) - Optional - Additional parameters for the request.

### Response
#### Success Response (200)
- **DataFrame** (pd.DataFrame) - A DataFrame containing the user's liquidation information.
```

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

### Place Multiple Orders from DataFrame

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Creates multiple orders simultaneously from a pandas DataFrame. Requires ccxt, pandas, and ccxt_pandas. Ensure sandbox mode is enabled for testing.

```python
import ccxt
import pandas as pd
from ccxt_pandas import CCXTPandasExchange

exchange = ccxt.binance({
    "apiKey": "your_api_key",
    "secret": "your_secret"
})
exchange.set_sandbox_mode(True)
pandas_exchange = CCXTPandasExchange(exchange=exchange)

# Get current price for reference
ohlcv = pandas_exchange.fetch_ohlcv(symbol="BTC/USDT", timeframe="1m", limit=1)
current_price = ohlcv["close"].iloc[-1]

# Create order DataFrame
orders = pd.DataFrame({
    "symbol": ["BTC/USDT"] * 4,
    "type": ["limit"] * 4,
    "side": ["buy", "buy", "sell", "sell"],
    "price": [
        current_price * 0.95,  # 5% below
        current_price * 0.90,  # 10% below
        current_price * 1.05,  # 5% above
        current_price * 1.10   # 10% above
    ],
    "cost": [100, 100, 100, 100]  # $100 each
})

# Submit all orders
results = pandas_exchange.create_orders(orders=orders)
print(results[["id", "symbol", "side", "price", "amount", "status"]])
```

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

### Fetch Order

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/_autosummary/crypto_pandas.utils.ccxt_pandas_exchange_typed.md

Retrieves details for a specific order ID and returns it as a dictionary.

```APIDOC
## POST /fetch_order

### Description
Retrieves details for a specific order ID and returns it as a dictionary.

### Method
POST

### Endpoint
/fetch_order

### Parameters
#### Query Parameters
- **id** (str) - Required - The ID of the order to retrieve.
- **symbol** (str | None) - Optional - The symbol associated with the order.
- **params** (dict) - Optional - Additional parameters for the request.

### Response
#### Success Response (200)
- **dict** - A dictionary containing the order details.
```

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

### Fetch Private Account Data (Trades, Positions, Greeks)

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/examples.md

Retrieve private account data including trades, positions, and greeks from a sandbox environment. Ensure you have set up API keys and enabled sandbox mode.

```python
import ccxt_pandas as cpd

# Load exchange in sandbox mode
exchange = cpd.load_exchange('binance', sandbox=True)

# Fetch trades
trades = exchange.fetch_my_trades()
print('My Trades:', trades)

# Fetch positions
positions = exchange.fetch_positions()
print('My Positions:', positions)

# Fetch greeks (if supported)
greeks = exchange.fetch_greeks()
print('My Greeks:', greeks)

```

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

### Stream Real-Time Trades

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Uses the AsyncCCXTPandasExchange to watch live trade data via WebSockets.

```python
import asyncio
import ccxt.pro as ccxt_pro
from ccxt_pandas import AsyncCCXTPandasExchange

exchange = ccxt_pro.binance()
pandas_exchange = AsyncCCXTPandasExchange(exchange=exchange)

async def stream_trades():
    try:
        while True:
            trades = await pandas_exchange.watch_trades(symbol="BTC/USDT")
            print(trades[["timestamp", "price", "amount", "side"]].tail(5))
    finally:
        await exchange.close()

asyncio.run(stream_trades())
```

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

### Currency and Conversion API

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/_autosummary/crypto_pandas.utils.async_ccxt_pandas_exchange_typed.md

Methods for fetching currency information and conversion details.

```APIDOC
## GET /api/v1/currencies

### Description
Fetches information about available currencies.

### Method
GET

### Endpoint
/api/v1/currencies

### Parameters
#### Query Parameters
- **params** (dict) - Optional - Additional parameters for the exchange.

### Response
#### Success Response (200)
- **DataFrame** (pd.DataFrame) - A DataFrame containing currency information.

#### Response Example
```json
{
  "example": "[DataFrame representing currency information]"
}
```

## GET /api/v1/convert/currencies

### Description
Fetches information about currencies supported for conversion.

### Method
GET

### Endpoint
/api/v1/convert/currencies

### Parameters
#### Query Parameters
- **params** (dict) - Optional - Additional parameters for the exchange.

### Response
#### Success Response (200)
- **DataFrame** (pd.DataFrame) - A DataFrame containing information about convertible currencies.

#### Response Example
```json
{
  "example": "[DataFrame representing convertible currencies]"
}
```

## GET /api/v1/convert/trade_history

### Description
Fetches the history of conversion trades.

### Method
GET

### Endpoint
/api/v1/convert/trade_history

### Parameters
#### Query Parameters
- **code** (str | None) - Optional - Filter by currency code.
- **since** (int | Timestamp | dict | str | None) - Optional - The earliest timestamp to fetch conversion history from.
- **limit** (int | None) - Optional - The maximum number of conversion trades to return.
- **params** (dict) - Optional - Additional parameters for the exchange.

### Response
#### Success Response (200)
- **DataFrame** (pd.DataFrame) - A DataFrame containing conversion trade history.

#### Response Example
```json
{
  "example": "[DataFrame representing conversion trade history]"
}
```
```

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

### fetch_bids_asks - Fetch Best Bid/Ask Prices

Source: https://context7.com/sigma-quantiphi/ccxt-pandas/llms.txt

Retrieves the best bid and ask prices, along with their volumes, for all trading symbols.

```APIDOC
## GET /api/v1/bids_asks

### Description
Retrieves the best bid and ask prices, and their corresponding volumes, for all available trading symbols.

### Method
GET

### Endpoint
/api/v1/bids_asks

### Parameters
This endpoint does not accept any parameters.

### Request Example
```python
# Fetch best bid/ask for all symbols
bids_asks = exchange.fetch_bids_asks()
```

### Response
#### Success Response (200)
- **symbol** (string) - Trading symbol
- **bid** (float) - Best bid price
- **ask** (float) - Best ask price
- **bidVolume** (float) - Volume available at the best bid price
- **askVolume** (float) - Volume available at the best ask price

#### Response Example
```json
{
  "symbol": "BTC/USDT",
  "bid": 42149.50,
  "ask": 42150.50,
  "bidVolume": 1.234567,
  "askVolume": 0.987654
}
```
```

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

### calculate_realized_pnl()

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/ccxt_pandas/calculations/README.md

Calculate realized PnL metrics by matching buy and sell trades.

```APIDOC
## calculate_realized_pnl()

### Description
Calculate realized PnL metrics by matching buy and sell trades.

### Parameters
- **trades** (DataFrame) - Required - Trades DataFrame from fetch_my_trades()
- **group_by** (tuple/list) - Optional - Columns to group by (default: ("symbol",))
- **freq** (string) - Optional - pandas frequency string for time aggregation
- **include_totals** (bool) - Optional - Whether to include "All" totals row (default: False)

### Response
- **Returns** (DataFrame) - Buy/sell amounts, prices, spread, matched amounts, and realized PnL
```

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

### Fetch and Plot OHLCV Data

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/README.md

Initializes a CCXTPandasExchange instance to fetch OHLCV data and plot the closing price.

```python
import ccxt
from ccxt_pandas import CCXTPandasExchange

exchange = CCXTPandasExchange(exchange=ccxt.binance())
ohlcv = exchange.fetch_ohlcv("BTC/USDT", timeframe="1m", limit=1000)
plt = ohlcv.close.plot(title="BTC/USDT — 1m")
plt.show()
```

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

### Fetch Leverages

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/docs/source/_autosummary/crypto_pandas.utils.ccxt_pandas_exchange_typed.md

Retrieves leverage information for specified symbols and returns it as a Pandas DataFrame.

```APIDOC
## POST /fetch_leverages

### Description
Retrieves leverage information for specified symbols and returns it as a Pandas DataFrame.

### Method
POST

### Endpoint
/fetch_leverages

### Parameters
#### Query Parameters
- **symbols** (List[str] | None) - Optional - A list of symbols to fetch leverage information for.
- **params** (dict) - Optional - Additional parameters for the request.

### Response
#### Success Response (200)
- **DataFrame** (pd.DataFrame) - A DataFrame containing the leverage information.
```

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

### calculate_vwap_by_depth()

Source: https://github.com/sigma-quantiphi/ccxt-pandas/blob/main/ccxt_pandas/calculations/README.md

Calculate Volume-Weighted Average Price (VWAP) at various depth levels.

```APIDOC
## calculate_vwap_by_depth()

### Description
Calculate Volume-Weighted Average Price (VWAP) at various depth levels. Useful for estimating market impact and slippage.

### Parameters
- **df** (DataFrame) - Required - Order book DataFrame with price, qty, symbol, and side columns
- **depths** (list) - Required - List of notional depths to calculate VWAP for
- **group_by** (list) - Optional - Columns to group by (default: ['symbol', 'side'] + 'exchange')
- **price_col** (string) - Optional - Name of price column (default: 'price')
- **qty_col** (string) - Optional - Name of quantity column (default: 'qty')

### Response
- **Returns** (DataFrame) - Columns: [*group_by, depth, qty, notional, price] where 'price' is the VWAP
```