Short-term market sentiment

trends/market_index_sentiment.py

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+import os
+import math
+import numpy as np
+import pandas as pd
+import datetime
+from datetime import date
+import pandas_ta as ta
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import datetime
+from yahoo_fin import stock_info as si
+from pandas.tseries.holiday import USFederalHolidayCalendar
+from pandas.tseries.offsets import CustomBusinessDay
+US_BUSINESS_DAY = CustomBusinessDay(calendar=USFederalHolidayCalendar())
+
+
+def calculate_smas(df):
+    df['SMA20'] = ta.sma(df["close"], length=20)
+    df['SMA5'] = ta.sma(df["close"], length=5)
+    df['SMA5_SMA20'] = df['SMA5'] / df['SMA20']
+    df['SMA5_SMA20_NORM'] = (df['SMA5_SMA20'] - df['SMA5_SMA20'].min()) / (df['SMA5_SMA20'].max() - df['SMA5_SMA20'].min())
+
+    return df
+
+
+def determine_market_trend(df):
+    short_term_sentiment = round(df['SMA5_SMA20_NORM'].tail(20).mean(),2)
+
+    return short_term_sentiment
+
+
+def load_daily_data(symbol):
+    today = datetime.date.today()
+    today_str = today.strftime("%Y-%m-%d")
+    start_date = today - (220 * US_BUSINESS_DAY)
+    start_date_str = datetime.datetime.strftime(start_date, "%Y-%m-%d")
+    try:
+        # Download data from Yahoo Finance
+        df = si.get_data(symbol, start_date=start_date_str, end_date=today_str, index_as_date=False)
+        return df
+    except:
+        print('Error loading stock data for ' + symbol)
+        return None
+
+
+def plot_trend_indicator(df, name):
+    fig = make_subplots(rows=2, cols=1)
+
+    #  Plot close price
+    fig.add_trace(go.Line(x=df.index, y=df['close'], line=dict(color="blue", width=1), name=name),
+                  row=1, col=1)
+
+    #  SMAs
+    fig.add_trace(go.Line(x=df.index, y=df['SMA20'], line=dict(color="#f95738", width=1), name="SMA 20"),
+                  row=1, col=1)
+    fig.add_trace(go.Line(x=df.index, y=df['SMA5'], line=dict(color="#ee964b", width=1), name="SMA 5"),
+                  row=1, col=1)
+
+
+    #  Plot Indicator
+    fig.add_trace(go.Line(x=df.index, y=df['SMA5_SMA20_NORM'], line=dict(color="#f95738", width=1), name="Short Term Trend Indicator"),
+                  row=2, col=1)
+
+
+    fig.add_hline(y=0.5, row=2, col=1)
+
+    fig.update_layout(
+        title={'text': f"{name} Trend Indicator", 'x': 0.5},
+        autosize=True,
+        width=800, height=800)
+    fig.update_yaxes(autorange=True, fixedrange=False, secondary_y=True, row=1, col=1)
+    fig.update_yaxes(range=[0,1], row=2, col=1)
+
+    fig.write_image(f"{name}_trend_indictor.png")
+
+
+
+def get_market_sentiment():
+    #  Retrieve prices for DOW
+    dji_df = load_daily_data('^DJI')
+    dji_df = calculate_smas(dji_df)
+    dow_short_term_trend = determine_market_trend(dji_df)
+
+    #  NASDAQ
+    nasdaq_df = load_daily_data('^IXIC')
+    nasdaq_df = calculate_smas(nasdaq_df)
+    nasdaq_short_term_trend = determine_market_trend(nasdaq_df)
+
+    #  S&P 500
+    snp_df = load_daily_data('^GSPC')
+    snp_df = calculate_smas(snp_df)
+    snp_short_term_trend = determine_market_trend(snp_df)
+
+    #  Crypto index
+    crypto_df = load_daily_data('BITW')
+    crypto_df = calculate_smas(crypto_df)
+    crypto_short_term_trend = determine_market_trend(crypto_df)
+
+    #  Visualize results
+    plot_trend_indicator(dji_df.tail(200), 'DJI')
+    plot_trend_indicator(nasdaq_df.tail(200), 'NASDAQ')
+    plot_trend_indicator(snp_df.tail(200), 'S&P')
+    plot_trend_indicator(crypto_df.tail(200), 'BITW')
+
+    combined_trend = (dow_short_term_trend + nasdaq_short_term_trend + snp_short_term_trend + crypto_short_term_trend) / 4
+    combined_trend = str(round(combined_trend,2))
+    print(combined_trend)
+
+get_market_sentiment()