add analyst insight endpoint

app/cron_analyst_insight.py

@@ -0,0 +1,113 @@
+from openai import OpenAI
+import time
+import ujson
+import sqlite3
+import requests
+import os
+from dotenv import load_dotenv
+from tqdm import tqdm
+from datetime import datetime
+
+# Load environment variables
+load_dotenv()
+
+# Initialize OpenAI client
+
+benzinga_api_key = os.getenv('BENZINGA_API_KEY')
+
+openai_api_key = os.getenv('OPENAI_API_KEY')
+org_id = os.getenv('OPENAI_ORG')
+client = OpenAI(
+  api_key=openai_api_key,
+  organization=org_id,
+)
+
+
+headers = {"accept": "application/json"}
+url = "https://api.benzinga.com/api/v1/analyst/insights"
+
+
+def save_json(symbol, data):
+    with open(f"json/analyst/insight/{symbol}.json", 'w') as file:
+        ujson.dump(data, file)
+
+def get_analyst_insight(ticker):
+
+    res_dict = {}
+
+    try:
+        querystring = {"token": benzinga_api_key,"symbols": ticker}
+        response = requests.request("GET", url, params=querystring)
+        output = ujson.loads(response.text)['analyst-insights'][0] #get the latest insight only
+        # Extracting required fields
+        res_dict = {
+            'insight': output['analyst_insights'],
+            'id': output['id'],
+            'date': datetime.strptime(output['date'], "%Y-%m-%d").strftime("%b %d, %Y")
+        }
+    except:
+        pass
+
+    return res_dict
+
+
+# Function to summarize the text using GPT-3.5-turbo
+def get_summary(data):
+    # Define the data to be summarized
+
+    # Format the data as a string
+    data_string = (
+        f"Insights: {data['insight']}"
+    )
+
+    response = client.chat.completions.create(
+        model="gpt-3.5-turbo-0125",
+        messages=[
+            {"role": "system", "content": "Summarize analyst insights clearly and concisely in under 400 characters. Ensure the summary is professional and easy to understand. Conclude with whether the report is bullish or bearish."},
+            {"role": "user", "content": data_string}
+        ],
+        max_tokens=150,
+        temperature=0.7
+    )
+
+
+    summary = response.choices[0].message.content
+    data = {
+            'insight': summary,
+            'id': data['id'],
+            'date': data['date']
+    }
+
+    return data
+
+
+try:
+    stock_con = sqlite3.connect('stocks.db')
+    stock_cursor = stock_con.cursor()
+    stock_cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 100E6 AND symbol NOT LIKE '%.%'")
+    stock_symbols = [row[0] for row in stock_cursor.fetchall()]
+
+
+    stock_con.close()
+
+    for symbol in tqdm(stock_symbols):
+        try:
+            data = get_analyst_insight(symbol)
+            new_report_id = data.get('id', '')
+            try:
+                with open(f"json/analyst/insight/{symbol}.json", 'r') as file:
+                    old_report_id = ujson.load(file).get('id', '')
+            except:
+                old_report_id = ''
+            #check first if new report id exist already to save money before sending it to closedai company
+            if new_report_id != old_report_id and len(data['insight']) > 0:
+                res = get_summary(data)
+                save_json(symbol, res)
+            else:
+                print('skipped')
+        except:
+            pass
+
+
+except Exception as e:
+    print(e)

app/main.py

@@ -3020,6 +3020,25 @@ async def get_borrowed_share(data:TickerData):
     redis_client.expire(cache_key, 3600*3600)  # Set cache expiration time to 1 day
     return res
 
+
+@app.post("/analyst-insight")
+async def get_analyst_insight(data:TickerData):
+    ticker = data.ticker.upper()
+    cache_key = f"analyst-insight-{ticker}"
+    cached_result = redis_client.get(cache_key)
+    if cached_result:
+        return ujson.loads(cached_result)
+    try:
+        with open(f"json/analyst/insight/{ticker}.json", 'r') as file:
+            res = ujson.load(file)
+    except:
+        res = {}
+
+    redis_client.set(cache_key, ujson.dumps(res))
+    redis_client.expire(cache_key, 3600*3600)  # Set cache expiration time to 1 day
+    return res
+
+
 @app.post("/implied-volatility")
 async def get_clinical_trial(data:TickerData):
     ticker = data.ticker.upper()

app/primary_cron_job.py

@@ -270,6 +270,7 @@ def run_analyst_rating():
     if week <= 5:
         subprocess.run(["python3", "cron_analyst_db.py"])
         subprocess.run(["python3", "cron_analyst_ticker.py"])
+        subprocess.run(["python3", "cron_analyst_insight.py"])
         command = ["sudo", "rsync", "-avz", "-e", "ssh", "/root/backend/app/json/analyst", f"root@{useast_ip_address}:/root/backend/app/json"]
         subprocess.run(command)
 

app/quant-analysis/mc-simulation.py

@@ -0,0 +1,105 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+import numpy as np
+from datetime import datetime
+import yfinance as yf
+from tqdm import tqdm
+
+#correlated_stocks = ['AQN', 'PACB', 'ZI', 'IPG', 'EW']
+ticker = 'GME'
+start_date = datetime(2024,5,1)
+end_date = datetime.today()
+df = yf.download(ticker, start=start_date, end=end_date, interval="1d").reset_index()
+#df = df.rename(columns={'Adj Close': 'close', 'Date': 'date'})
+df['daily_return'] = df['Adj Close'].pct_change()
+df = df.dropna()
+
+
+
+fig, ax = plt.subplots(figsize=(14,5))
+
+ax.plot(df['Date'], df['daily_return']*100, linestyle='--', marker='o',color='blue',label='Daily Returns')
+
+legend = ax.legend(loc="best", shadow=True, fontsize=15)
+plt.xlabel("Date",fontsize = 14)
+plt.ylabel("Percentage %", fontsize=15)
+plt.grid(True)
+plt.savefig('daily_return.png')
+
+
+
+
+
+
+fig, ax = plt.subplots(figsize=(14,5))
+
+days = 365
+
+#delta t
+dt = 1/365
+
+mu = df['daily_return'].mean()
+
+sigma = df['daily_return'].std()
+
+#Function takes in stock price, number of days to run, mean and standard deviation values
+def stock_monte_carlo(start_price,days,mu,sigma):
+
+    price = np.zeros(days)
+    price[0] = start_price
+
+    shock = np.zeros(days)
+    drift = np.zeros(days)
+
+    for x in range(1,days):
+
+        #Shock and drift formulas taken from the Monte Carlo formula
+        shock[x] = np.random.normal(loc=mu*dt,scale=sigma*np.sqrt(dt))
+
+        drift[x] = mu * dt
+
+        #New price = Old price + Old price*(shock+drift)
+        price[x] = price[x-1] + (price[x-1] * (drift[x]+shock[x]))
+
+    return price
+
+start_price = df['Adj Close'].iloc[-1] #Taken from above
+
+
+
+for run in tqdm(range(200)):
+    ax.plot(stock_monte_carlo(start_price,days,mu,sigma))
+
+plt.xlabel('Days')
+plt.ylabel('Price')
+plt.title('Monte Carlo Analysis for GME')
+plt.savefig('simulation.png')
+
+
+
+
+fig, ax = plt.subplots(figsize=(14,5))
+runs = 10000
+
+simulations = np.zeros(runs)
+for run in tqdm(range(runs)):
+    simulations[run] = stock_monte_carlo(start_price,days,mu,sigma)[days-1]
+
+q = np.percentile(simulations,1)
+
+plt.hist(simulations,bins=200)
+
+plt.figtext(0.6,0.8,s="Start price: $%.2f" %start_price)
+
+plt.figtext(0.6,0.7,"Mean final price: $%.2f" % simulations.mean())
+
+plt.figtext(0.6,0.6,"VaR(0.99): $%.2f" % (start_price -q,))
+
+plt.figtext(0.15,0.6, "q(0.99): $%.2f" % q)
+
+plt.axvline(x=q, linewidth=4, color='r')
+
+plt.title(u"Final price distribution for Gamestop Stock after %s days" %days, weight='bold')
+plt.savefig('histogram.png')
+