Create app.py

app.py

@@ -0,0 +1,76 @@
+import gradio as gr
+from gradio.inputs import File as InputFile
+from gradio.outputs import File as OutputFile
+from yahoo_fin import stock_info as si
+import yfinance as yf
+import pandas as pd
+import pandas_ta as ta
+import csv
+import os
+
+def detect_head_and_shoulders(hist):
+    local_max = hist['Close'][-60:][((hist['Close'][-60:].shift(1) < hist['Close'][-60:]) & (hist['Close'][-60:].shift(-1) < hist['Close'][-60:]))]
+    if len(local_max) < 3:
+        return False
+    peaks = local_max[-3:]
+    if peaks[0] < peaks[1] and peaks[2] < peaks[1] and abs(peaks[0] - peaks[2]) < peaks[1]*0.05:
+        return True
+    return False
+
+def detect_inverted_head_and_shoulders(hist):
+    local_min = hist['Close'][-60:][((hist['Close'][-60:].shift(1) > hist['Close'][-60:]) & (hist['Close'][-60:].shift(-1) > hist['Close'][-60:]))]
+    if len(local_min) < 3:
+        return False
+    valleys = local_min[-3:]
+    if valleys[0] > valleys[1] and valleys[2] > valleys[1] and abs(valleys[0] - valleys[2]) < valleys[1]*0.05:
+        return True
+    return False
+
+def process_csv(csv_file):
+    all_tickers = []
+    with open(csv_file.name, 'r') as file:
+        reader = csv.reader(file)
+        next(reader)  # Skip the header
+        for row in reader:
+            all_tickers.append(row[0])  # Append the value in the first column
+
+    ttl_tickers = len(all_tickers)
+    filtered_tickers = []
+    for ticker in all_tickers:
+        try:
+            data = yf.Ticker(ticker)
+            hist = data.history(period="1y", actions=False)
+            if not hist.empty and hist['Volume'][-1] > 999999 and 9 <= hist['Close'][-1] <= 301:
+                hist.ta.ema(close='Close', length=20, append=True)
+                hist.ta.ema(close='Close', length=50, append=True)
+                hist.ta.ema(close='Close', length=100, append=True)
+                hist.ta.sma(close='Close', length=150, append=True)
+                stoch = hist.ta.stoch(high='High', low='Low', close='Close')
+                if all(hist['EMA_20'][-10:] > hist['EMA_50'][-10:]) and all(hist['EMA_50'][-10:] > hist['EMA_100'][-10:]) and all(hist['EMA_100'][-10:] > hist['SMA_150'][-10:]) and stoch['STOCHk_14_3_3'][-1] < 30:
+                    filtered_tickers.append([ticker, hist['Close'][-1], hist['Volume'][-1], 'Bullish', 'Head and Shoulders' if detect_head_and_shoulders(hist) else '', 'Inverted Head and Shoulders' if detect_inverted_head_and_shoulders(hist) else ''])
+                elif all(hist['EMA_20'][-10:] < hist['EMA_50'][-10:]) and all(hist['EMA_50'][-10:] < hist['EMA_100'][-10:]) and all(hist['EMA_100'][-10:] < hist['SMA_150'][-10:]) and stoch['STOCHk_14_3_3'][-1] > 70:
+                    filtered_tickers.append([ticker, hist['Close'][-1], hist['Volume'][-1], 'Bearish', 'Head and Shoulders' if detect_head_and_shoulders(hist) else '', 'Inverted Head and Shoulders' if detect_inverted_head_and_shoulders(hist) else ''])
+        except Exception as e:
+            print(f"An error occurred with ticker {ticker}: {e}")
+
+    df = pd.DataFrame(filtered_tickers, columns=['Ticker', 'Close', 'Volume', 'Trend', 'Head and Shoulders', 'Inverted Head and Shoulders'])
+    output_xlsx = 'ema_stacks_list.xlsx'
+    df.to_excel(output_xlsx, index=False)
+    output_txt = 'pyScreener.txt'
+    with open(output_txt, 'w') as f:
+        for ticker in df['Ticker']:
+            f.write(f"{ticker}\n")
+    return output_xlsx, output_txt
+
+iface = gr.Interface(
+    fn=process_csv, 
+    inputs=InputFile(label="Upload CSV"), 
+    outputs=[
+        OutputFile(label="Download XLSX"),
+        OutputFile(label="Download TXT")
+    ],
+    title="Stock Shortlist",
+    description="Upload a CSV file with columns: Ticker	| Description | Industry | Market Capitalization | Sector from TradingView and get shortlisted, stacked EMA trends in return."
+)
+
+iface.launch()