Multi-timeframe automation

[derivativesnyc]

Has anyone had experience with automating execution based on multi-timeframe filter signal generation?

 

[DaVinciFXGroup]

What is this? Can you tell us in more detail?

 

[derivativesnyc]

Just basic cross-timeframe alignment confirmation (lower/higher timeframe) for short/long term trend - how can that be translates into multi-series instead of single series code logic?

 

[MattK]

Example for automating trade execution based on multi-timeframe filter signal generation, specifically using a simple moving average crossover strategy for both lower (hourly) and higher (daily) timeframes:

# Parameters for moving averages
short_term_MA_period = 50
long_term_MA_period = 200

# Function to calculate moving average
def calculate_moving_average(data, period):
return sum(data[-period:]) / period

# Main function for checking signals and executing trade
def check_signals_and_execute_trade(daily_data, hourly_data):
# Calculate moving averages for daily timeframe
daily_short_MA = calculate_moving_average(daily_data, short_term_MA_period)
daily_long_MA = calculate_moving_average(daily_data, long_term_MA_period)

# Calculate moving averages for hourly timeframe
hourly_short_MA = calculate_moving_average(hourly_data, short_term_MA_period)
hourly_long_MA = calculate_moving_average(hourly_data, long_term_MA_period)

# Check for signals on daily timeframe
if daily_short_MA > daily_long_MA:
daily_trend = "up"
elif daily_short_MA < daily_long_MA:
daily_trend = "down"
else:
daily_trend = "neutral"

# Check for signals on hourly timeframe
if hourly_short_MA > hourly_long_MA:
hourly_signal = "buy"
elif hourly_short_MA < hourly_long_MA:
hourly_signal = "sell"
else:
hourly_signal = "neutral"

# Execute trade based on cross-timeframe alignment
if daily_trend == "up" and hourly_signal == "buy":
execute_trade("buy")
elif daily_trend == "down" and hourly_signal == "sell":
execute_trade("sell")
else:
print("No clear signal, no trade executed.")

# Function for executing a trade
def execute_trade(action):
print(f"Executing {action} trade based on multi-timeframe alignment")

# Sample data (for illustration purposes)
daily_price_data = [120, 121, 122, 123, 124, 125, 126, 127, 128, 129]
hourly_price_data = [128, 127, 129, 130, 131, 130, 129, 128, 127, 126]

# Check for signals and execute trade
check_signals_and_execute_trade(daily_price_data, hourly_price_data)

This demonstrates the basic logic of using moving averages to identify the trend on the daily timeframe and determining entry/exit points on the hourly timeframe. Actual code would be more complex and should include accurate price data, error handling, risk management, and potentially more sophisticated logic for signal confirmation and trade execution, and also you need skills in Python