Integrate with Reddit.

ticker_extractor.py

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+# ticker_extractor.py
+
+import re
+
+# A set of common English words and acronyms that look like stock tickers.
+# This helps reduce false positives.
+COMMON_WORDS_BLACKLIST = {
+    "A", "I", "DD", "CEO", "CFO", "CTO", "EPS", "IPO", "YOLO", "FOMO",
+    "TLDR", "EDIT", "THE", "AND", "FOR", "ARE", "BUT", "NOT", "YOU",
+    "ALL", "ANY", "CAN", "HAS", "NEW", "NOW", "OLD", "SEE", "TWO",
+    "WAY", "WHO", "WHY", "BIG", "BUY", "SELL", "HOLD", "BE", "GO",
+    "ON", "AT", "IN", "IS", "IT", "OF", "OR", "TO", "WE", "UP",
+    "OUT", "SO", "RH", "SEC", "IRS", "USA", "UK", "EU",
+    "AI", "ML", "AR", "VR", "NFT", "DAO", "WEB3", "ETH", "BTC",
+    "USD", "EUR", "GBP", "JPY", "CNY", "INR", "AUD", "CAD", "CHF",
+    "RUB", "ZAR", "BRL", "MXN", "HKD", "SGD", "NZD", "RSD",
+    "JPY", "KRW", "SEK", "NOK", "DKK", "PLN", "CZK", "HUF", "TRY",
+    "US", "IRA", "FDA", "SEC", "FBI", "CIA", "NSA", "NATO",
+}
+
+def extract_tickers(text):
+    """
+    Extracts potential stock tickers from a given piece of text.
+    A ticker is identified as a 1-5 character uppercase word, or a word prefixed with $.
+    """
+    # Regex to find potential tickers:
+    # 1. Words prefixed with $: $AAPL, $TSLA
+    # 2. All-caps words between 1 and 5 characters: GME, AMC
+    ticker_regex = r"\$[A-Z]{1,5}\b|\b[A-Z]{1,5}\b"
+
+    potential_tickers = re.findall(ticker_regex, text)
+
+    # Filter out common words and remove the '$' prefix
+    tickers = []
+    for ticker in potential_tickers:
+        cleaned_ticker = ticker.replace("$", "").upper()
+        if cleaned_ticker not in COMMON_WORDS_BLACKLIST:
+            tickers.append(cleaned_ticker)
+
+    return tickers