Streamlit app

.pre-commit-config.yaml

@@ -0,0 +1,42 @@
+repos:
+  - repo: https://github.com/kynan/nbstripout
+    rev: 3843f5f7c586fa6f0df81423f35d52e6af8e3039
+    hooks:
+      - id: nbstripout
+        args: [
+            --extra-keys,
+            "metadata.interpreter metadata.language_info.codemirror_mode
+            metadata.language_info.file_extension metadata.language_info.mimetype
+            metadata.language_info.nbconvert_exporter metadata.language_info.pygments_lexer
+            metadata.language_info.version metadata.kernelspec",
+            --keep-output,
+          ]
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.3.1
+    hooks:
+      - id: nbqa-black
+      - id: nbqa-flake8
+        args: []
+      - id: nbqa-isort
+      - id: nbqa-pyupgrade
+        args: [--py36-plus]
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1
+    hooks:
+      - id: check-added-large-files
+        args: [--maxkb, "4096"]
+      - id: check-yaml
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+  - repo: https://github.com/pycqa/flake8
+    rev: 4.0.1
+    hooks:
+      - id: flake8
+  - repo: https://github.com/pycqa/isort
+    rev: 5.9.3
+    hooks:
+      - id: isort
+  - repo: https://github.com/psf/black
+    rev: 22.3.0
+    hooks:
+      - id: black

app.py

@@ -0,0 +1,205 @@
+import re
+import time
+from datetime import datetime
+
+import pandas as pd
+import plotly.graph_objects as go
+import streamlit as st
+from PIL import Image
+from plotly.subplots import make_subplots
+
+# Read CSV file into pandas and extract timestamp data
+dfSentiment = pd.read_csv("./sentiment_data.csv", index_col=0)
+dfSentiment["timestamp"] = [
+    datetime.strptime(dt, "%Y-%m-%d") for dt in dfSentiment["timestamp"].tolist()
+]
+
+# Multi-select columns to build chart
+col_list = list(dfSentiment.columns)
+
+r_sentiment = re.compile(".*sentiment")
+sentiment_cols = [col for col in col_list if r_sentiment.match(col)]
+
+r_post = re.compile(".*post")
+post_list = [col for col in col_list if r_post.match(col)]
+
+r_perc = re.compile(".*perc")
+perc_list = list(filter(r_perc.match, col_list))
+
+r_close = re.compile(".*close")
+close_list = list(filter(r_close.match, col_list))
+
+r_volume = re.compile(".*volume")
+volume_list = list(filter(r_volume.match, col_list))
+
+sentiment_cols = sentiment_cols + post_list
+stocks_cols = perc_list + close_list + volume_list
+
+# Config for page
+st.set_page_config(
+    page_title="TSLA Stock Sentiment Analysis",
+    page_icon="✅",
+    layout="wide",
+)
+
+with st.sidebar:
+    # FourthBrain logo to sidebar
+    fourthbrain_logo = Image.open("./images/fourthbrain_logo.png")
+    st.image([fourthbrain_logo], width=300)
+
+    # Date selection filters
+    start_date_filter = st.date_input(
+        "Start Data",
+        min(dfSentiment["timestamp"]),
+        min_value=min(dfSentiment["timestamp"]),
+        max_value=max(dfSentiment["timestamp"]),
+    )
+
+    end_date_filter = st.date_input(
+        "End Date",
+        max(dfSentiment["timestamp"]),
+        min_value=min(dfSentiment["timestamp"]),
+        max_value=max(dfSentiment["timestamp"]),
+    )
+
+    sentiment_select = st.selectbox(
+        "Select Sentiment",
+        sentiment_cols,
+        index=0,
+    )
+    stock_select = st.selectbox(
+        "Select Stock Data",
+        stocks_cols,
+        index=0,
+    )
+
+# Banner with TSLA and Reddit images
+tsla_logo = Image.open("./images/tsla_logo.png")
+reddit_logo = Image.open("./images/reddit_logo.png")
+st.image([tsla_logo, reddit_logo], width=200)
+
+# dashboard title
+st.title("TSLA Stock Sentiment Analysis")
+
+## dataframe filter
+# start date
+dfSentiment = dfSentiment[
+    dfSentiment["timestamp"]
+    >= datetime(start_date_filter.year, start_date_filter.month, start_date_filter.day)
+]
+
+# end date
+dfSentiment = dfSentiment[
+    dfSentiment["timestamp"]
+    <= datetime(end_date_filter.year, end_date_filter.month, end_date_filter.day)
+]
+dfSentiment = dfSentiment.reset_index(drop=True)
+
+
+# creating a single-element container
+placeholder = st.empty()
+
+# near real-time / live feed simulation
+for i in range(1, len(dfSentiment) - 1):
+
+    # creating KPIs
+    last_close = dfSentiment["close"][i]
+    last_close_lag1 = dfSentiment["close"][i - 1]
+    last_sentiment = dfSentiment["sentiment_score"][i]
+    last_sentiment_lag1 = dfSentiment["sentiment_score"][i - 1]
+
+    with placeholder.container():
+
+        # create columns
+        kpi1, kpi2, kpi3 = st.columns(3)
+
+        # fill in those three columns with respective metrics or KPIs
+        kpi1.metric(
+            label="Sentiment Score",
+            value=round(last_sentiment, 3),
+            delta=round(last_sentiment_lag1, 3),
+        )
+
+        kpi2.metric(
+            label="Last Closing Price",
+            value=round(last_close),
+            delta=round(last_close - last_close_lag1),
+        )
+
+        # create two columns for charts
+        fig_col1, fig_col2 = st.columns(2)
+
+        with fig_col1:
+            # Add traces
+            fig = make_subplots(specs=[[{"secondary_y": True}]])
+
+            fig.add_trace(
+                go.Scatter(
+                    x=dfSentiment["timestamp"][0:i],
+                    y=dfSentiment[sentiment_select][0:i],
+                    name=sentiment_select,
+                    mode="lines",
+                    hoverinfo="none",
+                )
+            )
+
+            if sentiment_select.startswith("perc"):
+                yaxis_label = "Percent Change Sentiment"
+
+            elif sentiment_select in sentiment_cols:
+                yaxis_label = "Sentiment Score"
+
+            elif sentiment_select in post_list:
+                yaxis_label = "Volume"
+
+            fig.layout.yaxis.title = yaxis_label
+
+            if stock_select.startswith("perc"):
+                fig.add_trace(
+                    go.Scatter(
+                        x=dfSentiment["timestamp"][0:i],
+                        y=dfSentiment[stock_select][0:i],
+                        name=stock_select,
+                        mode="lines",
+                        hoverinfo="none",
+                        yaxis="y2",
+                    )
+                )
+                fig.layout.yaxis2.title = "% Change Stock Price ($US)"
+
+            elif stock_select == "volume":
+                fig.add_trace(
+                    go.Scatter(
+                        x=dfSentiment["timestamp"][0:i],
+                        y=dfSentiment[stock_select][0:i],
+                        name=stock_select,
+                        mode="lines",
+                        hoverinfo="none",
+                        yaxis="y2",
+                    )
+                )
+
+                fig.layout.yaxis2.title = "Shares Traded"
+
+            else:
+                fig.add_trace(
+                    go.Scatter(
+                        x=dfSentiment["timestamp"][0:i],
+                        y=dfSentiment[stock_select][0:i],
+                        name=stock_select,
+                        mode="lines",
+                        hoverinfo="none",
+                        yaxis="y2",
+                    )
+                )
+
+                fig.layout.yaxis2.title = "Stock Price ($USD)"
+
+            fig.layout.xaxis.title = "Timestamp"
+
+            # write the figure throught streamlit
+            st.write(fig)
+
+        st.markdown("### Detailed Data View")
+        st.dataframe(dfSentiment.iloc[:, 1:][0:i])
+        time.sleep(1)

requirements.txt

@@ -0,0 +1,4 @@
+pandas==1.4.2
+Pillow==9.2.0
+plotly==5.9.0
+streamlit==1.10.0

sentiment_data.csv

@@ -0,0 +1,104 @@
+,timestamp,counter,close,volume,sentiment_score,close_lag1,perc_change_close,sentiment_score_lag1,perc_change_sentiment,sentiment_SMA3mo
+1,2022-02-26,9,0.0,,0.96355273326238,809.87,-1.0,0.8938873600959778,0.07793529283032052,0.0
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