> For the complete documentation index, see [llms.txt](https://docs.openalgo.in/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.openalgo.in/trading-platform/python/ema-crossover-strategy.md).

# EMA Crossover Strategy

Here is a coding snippet of python based EMA Crossover strategy implementing using placesmartorder function.

```python
from openalgo import api
import pandas as pd
import numpy as np
import time
import threading
from datetime import datetime, timedelta

# Get API key from openalgo portal
api_key = 'your-openalgo-api-key'


# Set the strategy details and trading parameters
strategy = "EMA Crossover Python"
symbol = "BHEL"  # OpenAlgo Symbol
exchange = "NSE"
product = "MIS"
quantity = 1

# EMA periods
fast_period = 5
slow_period = 10

# Set the API Key
client = api(api_key=api_key, host='http://127.0.0.1:5000')

def calculate_ema_signals(df):
    """
    Calculate EMA crossover signals.
    """
    close = df['close']
    
    # Calculate EMAs
    ema_fast = close.ewm(span=fast_period, adjust=False).mean()
    ema_slow = close.ewm(span=slow_period, adjust=False).mean()
    
    # Create crossover signals
    crossover = pd.Series(False, index=df.index)
    crossunder = pd.Series(False, index=df.index)
    
    # Previous values of EMAs
    prev_fast = ema_fast.shift(1)
    prev_slow = ema_slow.shift(1)
    
    # Current values of EMAs
    curr_fast = ema_fast
    curr_slow = ema_slow
    
    # Generate crossover signals
    crossover = (prev_fast < prev_slow) & (curr_fast > curr_slow)
    crossunder = (prev_fast > prev_slow) & (curr_fast < curr_slow)
    
    return pd.DataFrame({
        'EMA_Fast': ema_fast,
        'EMA_Slow': ema_slow,
        'Crossover': crossover,
        'Crossunder': crossunder
    }, index=df.index)

def ema_strategy():
    """
    The EMA crossover trading strategy.
    """
    position = 0

    while True:
        try:
            # Dynamic date range: 7 days back to today
            end_date = datetime.now().strftime("%Y-%m-%d")
            start_date = (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")

            # Fetch 1-minute historical data using OpenAlgo
            df = client.history(
                symbol=symbol,
                exchange=exchange,
                interval="1m",
                start_date=start_date,
                end_date=end_date
            )

            # Check for valid data
            if df.empty:
                print("DataFrame is empty. Retrying...")
                time.sleep(15)
                continue

            # Verify required columns
            if 'close' not in df.columns:
                raise KeyError("Missing 'close' column in DataFrame")

            # Round the close column
            df['close'] = df['close'].round(2)

            # Calculate EMAs and signals
            signals = calculate_ema_signals(df)

            # Get latest signals
            crossover = signals['Crossover'].iloc[-2]  # Using -2 to avoid partial candle
            crossunder = signals['Crossunder'].iloc[-2]

            # Execute Buy Order
            if crossover and position <= 0:
                position = quantity
                response = client.placesmartorder(
                    strategy=strategy,
                    symbol=symbol,
                    action="BUY",
                    exchange=exchange,
                    price_type="MARKET",
                    product=product,
                    quantity=quantity,
                    position_size=position
                )
                print("Buy Order Response:", response)

            # Execute Sell Order
            elif crossunder and position >= 0:
                position = quantity * -1
                response = client.placesmartorder(
                    strategy=strategy,
                    symbol=symbol,
                    action="SELL",
                    exchange=exchange,
                    price_type="MARKET",
                    product=product,
                    quantity=quantity,
                    position_size=position
                )
                print("Sell Order Response:", response)

            # Log strategy information
            print("\nStrategy Status:")
            print("-" * 50)
            print(f"Position: {position}")
            print(f"LTP: {df['close'].iloc[-1]}")
            print(f"Fast EMA ({fast_period}): {signals['EMA_Fast'].iloc[-2]:.2f}")
            print(f"Slow EMA ({slow_period}): {signals['EMA_Slow'].iloc[-2]:.2f}")
            print(f"Buy Signal: {crossover}")
            print(f"Sell Signal: {crossunder}")
            print("-" * 50)

        except Exception as e:
            print(f"Error in strategy: {str(e)}")
            time.sleep(15)
            continue

        # Wait before the next cycle
        time.sleep(15)

if __name__ == "__main__":
    print(f"Starting {fast_period}/{slow_period} EMA Crossover Strategy...")
    ema_strategy()
```