tradexil 1.0.5
Official Python SDK for TradeXil Real-Time API - Access BTCUSDT data with 197+ technical indicators
License: MIT
Requires Python: >=3.7
Keywords: tradexil, trading, bitcoin, btc, crypto, cryptocurrency, api, technical-indicators, real-time, market-data, financial-data, trading-bot, algorithmic-trading

# TradeXil Python SDK

Official Python client library for the **TradeXil Real-Time API** - Access live BTCUSDT candle data with 197 pre-calculated technical indicators across 6 timeframes.

---

## Quick Start

### Installation

```bash
pip install tradexil
```

### Basic Usage

```python
from tradexil import TradeXilAPI

# Initialize client
api = TradeXilAPI(api_key="your-api-key-here")

# Get latest 1-hour candle
candle = api.get_latest_candle("1h", format="json")

print(f"Close: {candle['close']}")
print(f"RSI(14): {candle['RSI_14']}")
print(f"MACD: {candle['MACD_12_26_9_line']}")
```

---

## What You Get

- **Real-time BTCUSDT data** from Binance
- **6 timeframes**: 5m, 15m, 30m, 1h, 4h, 1d
- **197 technical indicators** pre-calculated
- **Two output formats**: JSON (lightweight) or Parquet (fast DataFrame)
- **Production-ready**: Sub-second latency, 99.9% uptime

### Available Indicators

The API provides 197 columns including:

**Trend Indicators**
- EMA (8, 21, 50, 100, 200)
- MACD (12-26-9, 26-52-18, 50-100-50)
- Supertrend (10-3, 20-5, 30-7)
- ADX, Aroon, Parabolic SAR, TRIX, Vortex

**Momentum Indicators**
- RSI (7, 14, 21)
- Stochastic (14-3-3, 21-5-5)
- CCI, CMO, MFI, ROC, Williams %R, Ultimate Oscillator

**Volatility Indicators**
- Bollinger Bands (20-2, 50-2.5)
- ATR (14, 21)
- Keltner Channels (20-2, 50-2.5)
- Donchian Channels

**Volume Indicators**
- OBV, CMF, VWAP, Volume SMA/EMA, A/D Line, VPVR, Ease of Movement

**Specialized Features**
- Market Structure (BOS, CHoCH, swing highs/lows)
- Fair Value Gaps (FVG)
- Order Blocks (bullish/bearish)
- Liquidity Zones
- Premium/Discount Zones
- Volume Profile (POC, VAH, VAL)
- Support/Resistance Levels
- Pivot Points (Standard, Fibonacci, Camarilla)

---

## Getting Your API Key

1. Visit [https://tradexil.com/api](https://tradexil.com/api)
2. Sign up or log in
3. Generate your API key
4. Start using the SDK immediately

**Free Tier**: 1,000 requests/day  
**Pro Tier**: Unlimited requests with priority access

---

## Documentation

### Initialize Client

```python
from tradexil import TradeXilAPI

api = TradeXilAPI(
    api_key="your-api-key-here",
    base_url="https://tradexil.com",  # Optional
    timeout=10  # Optional, seconds
)
```

### Get API Status

```python
status = api.get_status()
print(status)
# {
#   "status": "operational",
#   "available_timeframes": ["5m", "15m", "30m", "1h", "4h", "1d"],
#   "total_columns": 197
# }
```

### Get Latest Candle

**JSON Format (Lightweight)**

```python
candle = api.get_latest_candle("1h", format="json")

print(f"Timestamp: {candle['timestamp']}")
print(f"Close: {candle['close']}")
print(f"Volume: {candle['volume']}")
print(f"RSI: {candle['RSI_14']}")
```

**Parquet Format (Fast DataFrame)**

```python
df = api.get_latest_candle("5m", format="parquet")

# Access as DataFrame
print(df[['timestamp', 'close', 'RSI_14', 'MACD_12_26_9_line']])
print(df.shape)  # (1, 197)
```

### Get All Timeframes

```python
# Get latest candle for all timeframes
candles = api.get_all_timeframes(format="json")

for timeframe, candle in candles.items():
    print(f"{timeframe}: Close = {candle['close']}, RSI = {candle['RSI_14']}")
```

### Stream Candles (Polling)

**Generator Pattern**

```python
# Stream with generator
for candle in api.stream_candles("5m", interval=60):
    print(f"New 5m candle: Close = {candle['close']}")
```

**Callback Pattern**

```python
def handle_candle(candle):
    print(f"Timestamp: {candle['timestamp']}")
    print(f"Close: {candle['close']}")
    print(f"RSI: {candle['RSI_14']}")

# Stream with callback (blocks until Ctrl+C)
api.stream_candles("1h", interval=60, callback=handle_candle)
```

### Quick Helper Function

```python
from tradexil import get_latest_candle

# One-liner access (no client initialization needed)
candle = get_latest_candle("1h", api_key="your-key")
print(candle['close'])
```

---

## Error Handling

The SDK provides specific exception types for different error scenarios:

```python
from tradexil import (
    TradeXilAPI,
    TradeXilAuthError,
    TradeXilDataError,
    TradeXilAPIError
)

api = TradeXilAPI(api_key="your-key")

try:
    candle = api.get_latest_candle("1h")
    print(f"Close: {candle['close']}")

except TradeXilAuthError as e:
    print(f"Authentication failed: {e}")
    # Invalid or expired API key

except TradeXilDataError as e:
    print(f"Data not available: {e}")
    # Wait a few seconds and retry

except TradeXilAPIError as e:
    print(f"API error: {e}")
    # Other API errors

except Exception as e:
    print(f"Unexpected error: {e}")
```

**Exception Types:**
- `TradeXilAuthError`: Invalid or expired API key (HTTP 401)
- `TradeXilDataError`: Data not available yet (HTTP 503)
- `TradeXilAPIError`: General API errors (timeouts, connection issues, etc.)

---

## Requirements

- Python 3.7+
- `requests>=2.25.0`
- `pandas>=1.3.0`
- `pyarrow>=6.0.0`

All dependencies are automatically installed with `pip install tradexil`.

---

## Use Cases

### Algorithmic Trading

```python
api = TradeXilAPI(api_key="your-key")

def trading_strategy():
    candle = api.get_latest_candle("1h", format="json")
    
    rsi = candle['RSI_14']
    macd = candle['MACD_12_26_9_line']
    
    if rsi < 30 and macd > 0:
        return "BUY"
    elif rsi > 70 and macd < 0:
        return "SELL"
    else:
        return "HOLD"

signal = trading_strategy()
print(f"Signal: {signal}")
```

### Market Analysis

```python
# Get multi-timeframe view
candles = api.get_all_timeframes(format="parquet")

for tf, df in candles.items():
    rsi = df['RSI_14'].iloc[0]
    trend = "Bullish" if df['EMA_21'].iloc[0] > df['EMA_50'].iloc[0] else "Bearish"
    print(f"{tf}: RSI={rsi:.2f}, Trend={trend}")
```

### Data Pipeline

```python
import pandas as pd

def collect_data(timeframe="1h", samples=100):
    api = TradeXilAPI(api_key="your-key")
    data = []
    
    for candle in api.stream_candles(timeframe, interval=60):
        data.append(candle)
        if len(data) >= samples:
            break
    
    return pd.DataFrame(data)

# Collect 100 hourly candles
df = collect_data("1h", samples=100)
df.to_csv("btcusdt_1h.csv", index=False)
```

---

## Links

- **Website**: [https://tradexil.com](https://tradexil.com)
- **API Documentation**: [https://tradexil.com/api](https://tradexil.com/api)
- **Get API Key**: [https://tradexil.com/api](https://tradexil.com/api)
- **Support**: support@tradexil.com

---

## License

MIT License © 2025 TradeXil

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## Support

Need help? Have questions?

- **Email**: support@tradexil.com
- **Documentation**: [https://tradexil.com/api](https://tradexil.com/api)

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