experiment with spectrograms and mouse click tracking between plots

app.py

@@ -1,8 +1,22 @@
-import streamlit as st
+import streamlit
 
 import page_symbol_details
 
+# test
+import calculator
+from scipy.io import wavfile
+import ui
+from plotly.subplots import make_subplots
+import plotly.graph_objects as go
+import numpy as np
+from streamlit_plotly_events import plotly_events
+import plotly.express as px
+import streamlit as st
+
+# test end
+
+
+
 if __name__ == '__main__':
     st.set_page_config(layout="wide")
-
     page_symbol_details.run()

calculator.py

@@ -1,4 +1,5 @@
 import numpy as np
+import scipy as sp
 from datetime import datetime
 
 
@@ -52,4 +53,15 @@ def linear_regression(dates, y_list):
     y_low = m * dates[0] + c
     y_high = m * dates[-1] + c
 
-    return y_low, y_high
+    return y_low, y_high
+
+
+def normalize(data_list, high=1.0, low=-1.0):
+    data = np.array(data_list)
+    min_val = np.min(data)
+    max_val = np.max(data)
+    delta = max_val - min_val
+    new_delta = high - low
+
+    data = ((data - min_val) * new_delta / delta) + low
+    return data

page_symbol_details.py

@@ -1,6 +1,8 @@
 import numpy as np
 import streamlit as st
 from plotly.subplots import make_subplots
+from streamlit_plotly_events import plotly_events
+import plotly.graph_objects as go
 
 import data_retriever
 import trends
@@ -8,40 +10,43 @@ import ui
 
 
 def run():
-    exchange_names = data_retriever.get_exchange_code_names()
-    exchanges_selectbox = st.selectbox(
-        'Exchange:',
-        exchange_names,
-        index=exchange_names.index('US exchanges (NYSE, Nasdaq)')
-    )
-    exchange_name = exchanges_selectbox
-    exchange_index = exchange_names.index(exchange_name)
-    exchange = data_retriever.get_exchange_codes()[exchange_index]
-
-    symbols = data_retriever.get_symbols(exchange)
-    symbols_selectbox = st.selectbox(
-        'Stock:',
-        symbols, 
-        index=symbols.index('AAPL')
-    )
-    symbol = symbols_selectbox
-
-    # max time period
-    st.text_input("No. of years look-back", value=1, key="years_back")
-    years_back = int(st.session_state.years_back)
-    weeks_back = years_back*12*4
-
-    symbol_prices = data_retriever.get_current_stock_data(symbol, weeks_back)
-    if not any(symbol_prices):
-        return
-    dates = symbol_prices.index.format()
-
-    # back test
-    st.text_input('No. of days back-test', value=0, key="backtest_period")
-    n_days_back = int(st.session_state.backtest_period)
-    end_date = data_retriever.n_days_before(data_retriever.today(), n_days_back)
-    symbol_prices_backtest = symbol_prices[symbol_prices.index <= end_date]
-    backtest_dates = symbol_prices_backtest.index.format()
+    col1, col2 = st.columns(2)
+    with col1:
+        exchange_names = data_retriever.get_exchange_code_names()
+        exchanges_selectbox = st.selectbox(
+            'Exchange:',
+            exchange_names,
+            index=exchange_names.index('US exchanges (NYSE, Nasdaq)')
+        )
+        exchange_name = exchanges_selectbox
+        exchange_index = exchange_names.index(exchange_name)
+        exchange = data_retriever.get_exchange_codes()[exchange_index]
+
+        symbols = data_retriever.get_symbols(exchange)
+        symbols_selectbox = st.selectbox(
+            'Stock:',
+            symbols,
+            index=symbols.index('AAPL')
+        )
+        symbol = symbols_selectbox
+
+    with col2:
+        # max time period
+        st.text_input('No. of years look-back:', value=1, key="years_back")
+        years_back = int(st.session_state.years_back)
+        weeks_back = years_back*12*4
+
+        symbol_prices = data_retriever.get_current_stock_data(symbol, weeks_back)
+        if not any(symbol_prices):
+            return
+        dates = symbol_prices.index.format()
+
+        # back test
+        st.text_input('No. of days back-test:', value=0, key="backtest_period")
+        n_days_back = int(st.session_state.backtest_period)
+        end_date = data_retriever.n_days_before(data_retriever.today(), n_days_back)
+        symbol_prices_backtest = symbol_prices[symbol_prices.index <= end_date]
+        backtest_dates = symbol_prices_backtest.index.format()
 
     # symbol candlestick graph
     candleFigure = make_subplots(rows=1, cols=1)
@@ -67,7 +72,20 @@ def run():
                                xaxis_title='Date',
                                yaxis_title="Price per Share",
                                template='plotly_dark')
-    candle_chart_id = st.plotly_chart(candleFigure, use_container_width=True, key='candle')
+    selected_points = plotly_events(candleFigure, click_event=True, hover_event=False, select_event=False, key='3')
+
+    if st.checkbox('Tests'):
+        testFigure = make_subplots(rows=1, cols=1)
+        ui.create_line(testFigure, dates, symbol_prices['Volume'], "Volume")
+        ui.add_mouse_indicator(testFigure, selected_points, min=np.min(symbol_prices['Volume']),
+                               max=np.max(symbol_prices['Volume']))
+        test_id = st.plotly_chart(testFigure, use_container_width=True, key='test')
+
+        fft_steps = st.slider(label='FFT Steps', min_value=8, max_value=128, value=16, step=8)
+        specFig1 = ui.create_spectrogram(dates, symbol_prices['Close'], sampling_frequency=1, num_points_fft=fft_steps,
+                                         overlap_percent=0.0, title="Close Price Spectrogram")
+        specFig2 = ui.create_spectrogram(dates, symbol_prices['Volume'], sampling_frequency=1, num_points_fft=fft_steps,
+                                         overlap_percent=0.0, title="Volume Spectrogram")
 
     if st.checkbox('Sector Trends'):
         # plot the trend of the market as a candlestick graph.

requirements.txt

@@ -1,4 +1,5 @@
 streamlit
+streamlit_plotly_events
 
 yfinance
 finnhub-python
@@ -9,3 +10,4 @@ datetime
 plotly
 
 numpy
+scipy

trends.py

@@ -33,13 +33,8 @@ def sector_trends(symbol, weeks_back):
             indicator_stock_data_normalized_sum = indicator_stock_data_normalized_peer_sum[indicator]
 
             indicator_values = peer_stock_data[indicator]
-            min_val = indicator_values[0]
-            max_val = indicator_values[0]
-            for value in indicator_values:
-                if value < min_val:
-                    min_val = value
-                if value > max_val:
-                    max_val = value
+            min_val = np.min(indicator_values)
+            max_val = np.max(indicator_values)
             delta = max_val - min_val
 
             value_idx = 0

ui.py

@@ -4,6 +4,11 @@ import plotly.graph_objects as go
 from plotly.validators.scatter.marker import SymbolValidator
 from plotly.subplots import make_subplots
 
+import numpy as np
+from scipy import signal
+
+import calculator
+
 # chart datapoint icons
 raw_symbols = SymbolValidator().values
 up_arrow = raw_symbols[5]
@@ -76,7 +81,7 @@ def create_markers(fig, dates, dataset, title, y_label, marker_symbol=3, marker_
     fig.add_trace(line)
 
 
-def create_line(fig, dates, dataset, title, y_label, marker_symbol=4, marker_size=15):
+def create_line(fig, dates, dataset, title="title", y_label="values", marker_symbol=4, marker_size=15):
     line = go.Scatter(name=title, x=dates, y=dataset, marker_line_color="yellow")
     fig.add_trace(line)
 
@@ -95,3 +100,42 @@ def create_fill_area(fig, dates, y_low, y_high, title, color='rgba(0,100,80,0.2)
         line=dict(color=color)
     )
     fig.add_trace(fill_area)
+
+
+def create_spectrogram(dates, data_list, sampling_frequency=1, num_points_fft=128, overlap_percent=50.0, title="title"):
+    data_list = calculator.normalize(data_list, 1, -1)
+
+    # Spectrogram
+    w = signal.blackman(num_points_fft)
+    freqs, bins, pxx = signal.spectrogram(data_list, sampling_frequency, window=w, nfft=num_points_fft, noverlap=int(num_points_fft*overlap_percent/100.0))
+
+    dates_subset = [dates[int(bin)] for bin in bins]
+
+    trace = [go.Heatmap(
+        x=dates_subset,
+        y=freqs,
+        z=10 * np.log10(pxx),
+        colorscale='Jet',
+    )]
+    layout = go.Layout(
+        title=title,
+        yaxis=dict(title='Frequency'),  # x-axis label
+        xaxis=dict(title='Time'),  # y-axis label
+    )
+    fig = go.Figure(data=trace, layout=layout)
+    st.plotly_chart(fig, use_container_width=True)
+
+    return fig
+
+def add_mouse_indicator(fig, selected_points, min, max):
+    if any(selected_points):
+        fig.add_shape(
+                go.layout.Shape(
+                    type='line',
+                    x0=selected_points[0]['x'],
+                    x1=selected_points[0]['x'],
+                    y0=min,
+                    y1=max,
+                    line=dict(color='red', width=2),
+                )
+            )