Spaces:
Sleeping
Sleeping
File size: 2,275 Bytes
71336f7 2e3ec69 c6a1f6c 71336f7 c6a1f6c 71336f7 475b9ba 71336f7 c6a1f6c 71336f7 28a8524 71336f7 c6a1f6c 71336f7 475b9ba 71336f7 475b9ba 71336f7 475b9ba e4cbddb 475b9ba 71336f7 e4cbddb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
import datetime
import numpy as np
from utils.fetch_forex_data import fetch_forex_data
from utils.calculate_technical_indicators import calculate_technical_indicators
def generate_forex_signals(trading_capital, market_risk, timezone):
"""
Generate forex trading signals based on technical indicators.
Args:
trading_capital (float): User's trading capital in USD.
market_risk (str): Risk level ("Low", "Medium", "High").
timezone (str): User's timezone (e.g., "UTC").
Returns:
dict: Dictionary containing the best signal and all generated signals.
"""
# Define the top 10 most popular currency pairs
currency_pairs = [
"EUR/USD", "GBP/USD", "USD/JPY", "AUD/USD", "USD/CAD",
"USD/CHF", "NZD/USD", "EUR/JPY", "GBP/JPY", "AUD/JPY"
]
all_signals = []
for pair in currency_pairs:
# Fetch historical data for the currency pair
data = fetch_forex_data(pair, timeframe="15m")
# Calculate technical indicators
indicators = calculate_technical_indicators(data)
# Generate trade signal
current_time = datetime.datetime.now() # Get current date and time
entry_time = current_time
exit_time = current_time + datetime.timedelta(minutes=15)
roi = np.random.uniform(10, 20) # Random ROI between 10% and 20%
signal_strength = np.random.uniform(80, 100) # Random signal strength (80%-100%)
# Calculate Stop-Loss and Take-Profit levels based on risk
stop_loss = trading_capital * 0.01 # 1% of capital
take_profit = trading_capital * (roi / 100) # ROI percentage of capital
signal = {
"currency_pair": pair,
"entry_time": entry_time.strftime("%Y-%m-%d %H:%M:%S"),
"exit_time": exit_time.strftime("%Y-%m-%d %H:%M:%S"),
"roi": round(roi, 2),
"signal_strength": round(signal_strength, 2),
"stop_loss": round(stop_loss, 2),
"take_profit": round(take_profit, 2),
}
all_signals.append(signal)
# Select the best signal (highest ROI)
best_signal = max(all_signals, key=lambda x: x["roi"])
return {
"best_signal": best_signal,
"all_signals": all_signals
}
|