Delete utils/forex_signals.py

utils/forex_signals.py

@@ -1,89 +0,0 @@
-import requests
-import pandas as pd
-import numpy as np
-
-API_KEY = "89SEdLScHxHk6j8J9OoH4sLFS3Mri4oW"
-
-def fetch_forex_data(currency_pair):
-    try:
-        # Convert currency pair to API format (e.g., EUR/USD -> EURUSD)
-        formatted_pair = currency_pair.replace("/", "")
-        url = f"https://financialmodelingprep.com/api/v3/forex/{formatted_pair}?apikey={API_KEY}"
-        response = requests.get(url)
-        response.raise_for_status()
-
-        data = response.json()
-        if "historical" in data and len(data["historical"]) > 0:
-            df = pd.DataFrame(data["historical"])
-            df["close"] = pd.to_numeric(df["close"], errors="coerce")
-            df.dropna(inplace=True)
-            return df
-        else:
-            print(f"No data available for {currency_pair}.")
-            return None
-    except Exception as e:
-        print(f"Error fetching data for {currency_pair}: {str(e)}")
-        return None
-
-
-def calculate_indicators(df):
-    try:
-        df["SMA_50"] = df["close"].rolling(window=50).mean()
-        df["SMA_200"] = df["close"].rolling(window=200).mean()
-        df["RSI"] = compute_rsi(df["close"])
-        return df
-    except Exception as e:
-        print(f"Error calculating indicators: {str(e)}")
-        return None
-
-
-def compute_rsi(series, period=14):
-    delta = series.diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
-    rs = gain / loss
-    rsi = 100 - (100 / (1 + rs))
-    return rsi
-
-
-def generate_forex_signals(trading_capital, market_risk, currency_pairs):
-    all_signals = []
-    best_signal = None
-
-    for pair in currency_pairs:
-        print(f"Processing {pair}...")
-        df = fetch_forex_data(pair)
-        if df is None or df.empty:
-            continue
-
-        df = calculate_indicators(df)
-        if df is None or df.empty:
-            continue
-
-        if df["SMA_50"].iloc[-1] > df["SMA_200"].iloc[-1] and df["RSI"].iloc[-1] < 70:
-            signal = {
-                "currency_pair": pair,
-                "entry_time": df["date"].iloc[-1],
-                "exit_time": df["date"].iloc[-1],  # Placeholder; use a model to predict exit
-                "roi": np.random.uniform(1, 3),  # Simulated ROI
-                "signal_strength": "Strong Buy"
-            }
-            all_signals.append(signal)
-        elif df["SMA_50"].iloc[-1] < df["SMA_200"].iloc[-1] and df["RSI"].iloc[-1] > 30:
-            signal = {
-                "currency_pair": pair,
-                "entry_time": df["date"].iloc[-1],
-                "exit_time": df["date"].iloc[-1],  # Placeholder; use a model to predict exit
-                "roi": np.random.uniform(-3, -1),  # Simulated ROI
-                "signal_strength": "Strong Sell"
-            }
-            all_signals.append(signal)
-
-    # Determine the best signal based on ROI
-    if all_signals:
-        best_signal = max(all_signals, key=lambda x: x["roi"])
-
-    return {
-        "all_signals": all_signals,
-        "best_signal": best_signal
-    }