Lots of improvements and adding a script to post to reddit.
This commit is contained in:
1
.gitignore
vendored
1
.gitignore
vendored
@@ -6,3 +6,4 @@ __pycache__/
|
|||||||
*.db
|
*.db
|
||||||
*.log
|
*.log
|
||||||
reddit_stock_analyzer.egg-info/
|
reddit_stock_analyzer.egg-info/
|
||||||
|
images/
|
164
README.md
164
README.md
@@ -156,3 +156,167 @@ This command starts a local web server to let you explore the data you've collec
|
|||||||
* **Subreddit Pages:** Click any subreddit in the navigation bar to see a dashboard specific to that community.
|
* **Subreddit Pages:** Click any subreddit in the navigation bar to see a dashboard specific to that community.
|
||||||
* **Deep Dive:** In any table, click on a ticker's symbol to see a detailed breakdown of every post it was mentioned in.
|
* **Deep Dive:** In any table, click on a ticker's symbol to see a detailed breakdown of every post it was mentioned in.
|
||||||
* **Shareable Images:** On a subreddit's page, click "(View Daily Image)" or "(View Weekly Image)" to generate a polished, shareable summary card.
|
* **Shareable Images:** On a subreddit's page, click "(View Daily Image)" or "(View Weekly Image)" to generate a polished, shareable summary card.
|
||||||
|
|
||||||
|
|
||||||
|
### 3. Exporting Shareable Images (`.png`)
|
||||||
|
|
||||||
|
In addition to viewing the dashboards in a browser, the project includes a powerful script to programmatically save the 'image views' as static `.png` files. This is ideal for automation, scheduled tasks (cron jobs), or sharing the results on social media platforms like your `r/rstat` subreddit.
|
||||||
|
|
||||||
|
#### One-Time Setup
|
||||||
|
|
||||||
|
The image exporter uses the Playwright library to control a headless browser. Before using it for the first time, you must install the necessary browser runtimes with this command:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
playwright install
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Usage Workflow
|
||||||
|
|
||||||
|
The exporter works by taking a high-quality screenshot of the live web page. Therefore, the process requires two steps running in two separate terminals.
|
||||||
|
|
||||||
|
**Step 1: Start the Web Dashboard**
|
||||||
|
|
||||||
|
The web server must be running for the exporter to have a page to screenshot. Open a terminal and run:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
rstat-dashboard
|
||||||
|
```
|
||||||
|
Leave this terminal running.
|
||||||
|
|
||||||
|
**Step 2: Run the Export Script**
|
||||||
|
|
||||||
|
Open a **second terminal** in the same project directory. You can now run the `export_image.py` script with the desired arguments.
|
||||||
|
|
||||||
|
**Examples:**
|
||||||
|
|
||||||
|
* To export the **daily** summary image for `r/wallstreetbets`:
|
||||||
|
```bash
|
||||||
|
python export_image.py wallstreetbets
|
||||||
|
```
|
||||||
|
|
||||||
|
* To export the **weekly** summary image for `r/wallstreetbets`:
|
||||||
|
```bash
|
||||||
|
python export_image.py wallstreetbets --weekly
|
||||||
|
```
|
||||||
|
|
||||||
|
* To export the **overall** summary image (across all subreddits):
|
||||||
|
```bash
|
||||||
|
python export_image.py --overall
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Output
|
||||||
|
|
||||||
|
After running a command, a new `.png` file (e.g., `wallstreetbets_daily_1690000000.png`) will be saved in the images-directory in the root directory of the project.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## 4. Full Automation: Posting to Reddit via Cron Job
|
||||||
|
|
||||||
|
The final piece of the project is a script that automates the entire process: scraping data, generating an image, and posting it to a target subreddit like `r/rstat`. This is designed to be run via a scheduled task or cron job.
|
||||||
|
|
||||||
|
### Prerequisites for Posting
|
||||||
|
|
||||||
|
The posting script needs to log in to your Reddit account. You must add your Reddit username and password to your `.env` file.
|
||||||
|
|
||||||
|
**Add these two lines to your `.env` file:**
|
||||||
|
```
|
||||||
|
REDDIT_USERNAME=YourRedditUsername
|
||||||
|
REDDIT_PASSWORD=YourRedditPassword
|
||||||
|
```
|
||||||
|
*(For security, it's recommended to use a dedicated bot account for this, not your personal account.)*
|
||||||
|
|
||||||
|
### The `post_to_reddit.py` Script
|
||||||
|
|
||||||
|
This is a standalone script located in the project's root directory that finds the most recently generated image and posts it to Reddit.
|
||||||
|
|
||||||
|
**Manual Usage:**
|
||||||
|
|
||||||
|
You can run this script manually from your terminal. This is great for testing or one-off posts.
|
||||||
|
|
||||||
|
* **Post the latest OVERALL summary image to `r/rstat`:**
|
||||||
|
```bash
|
||||||
|
python post_to_reddit.py
|
||||||
|
```
|
||||||
|
|
||||||
|
* **Post the latest DAILY image for a specific subreddit:**
|
||||||
|
```bash
|
||||||
|
python post_to_reddit.py --subreddit wallstreetbets
|
||||||
|
```
|
||||||
|
|
||||||
|
* **Post the latest WEEKLY image for a specific subreddit:**
|
||||||
|
```bash
|
||||||
|
python post_to_reddit.py --subreddit wallstreetbets --weekly
|
||||||
|
```
|
||||||
|
|
||||||
|
* **Post to a different target subreddit (e.g., a test subreddit):**
|
||||||
|
```bash
|
||||||
|
python post_to_reddit.py --target-subreddit MyTestSub
|
||||||
|
```
|
||||||
|
|
||||||
|
### Setting Up the Cron Job for Full Automation
|
||||||
|
|
||||||
|
To run the entire pipeline automatically every day, you can use a simple shell script controlled by `cron`.
|
||||||
|
|
||||||
|
**Step 1: Create a Job Script**
|
||||||
|
|
||||||
|
Create a file named `run_daily_job.sh` in the root of your project directory. This script will run all the necessary commands in the correct order.
|
||||||
|
|
||||||
|
**`run_daily_job.sh`:**
|
||||||
|
```bash
|
||||||
|
#!/bin/bash
|
||||||
|
|
||||||
|
# CRITICAL: Navigate to the project directory using an absolute path.
|
||||||
|
# Replace '/path/to/your/project/reddit_stock_analyzer' with your actual path.
|
||||||
|
cd /path/to/your/project/reddit_stock_analyzer
|
||||||
|
|
||||||
|
# CRITICAL: Activate the virtual environment using an absolute path.
|
||||||
|
source /path/to/your/project/reddit_stock_analyzer/.venv/bin/activate
|
||||||
|
|
||||||
|
echo "--- Starting RSTAT Daily Job on $(date) ---"
|
||||||
|
|
||||||
|
# 1. Scrape data from the last 24 hours for all subreddits in the config.
|
||||||
|
echo "Step 1: Scraping new data..."
|
||||||
|
rstat --config subreddits.json --days 1
|
||||||
|
|
||||||
|
# 2. Start the dashboard in the background so the exporter can access it.
|
||||||
|
echo "Step 2: Starting dashboard in background..."
|
||||||
|
rstat-dashboard &
|
||||||
|
DASHBOARD_PID=$!
|
||||||
|
|
||||||
|
# Give the server a moment to start up.
|
||||||
|
sleep 10
|
||||||
|
|
||||||
|
# 3. Export the overall summary image.
|
||||||
|
echo "Step 3: Exporting overall summary image..."
|
||||||
|
python export_image.py --overall
|
||||||
|
|
||||||
|
# 4. Post the newly created overall summary image to r/rstat.
|
||||||
|
echo "Step 4: Posting image to Reddit..."
|
||||||
|
python post_to_reddit.py --target-subreddit rstat
|
||||||
|
|
||||||
|
# 5. Clean up by stopping the background dashboard server.
|
||||||
|
echo "Step 5: Stopping dashboard server..."
|
||||||
|
kill $DASHBOARD_PID
|
||||||
|
|
||||||
|
echo "--- RSTAT Daily Job Complete ---"
|
||||||
|
```**Before proceeding, you must edit the two absolute paths at the top of this script to match your system.**
|
||||||
|
|
||||||
|
**Step 2: Make the Script Executable**
|
||||||
|
|
||||||
|
In your terminal, run the following command:
|
||||||
|
```bash
|
||||||
|
chmod +x run_daily_job.sh
|
||||||
|
```
|
||||||
|
|
||||||
|
**Step 3: Schedule the Cron Job**
|
||||||
|
|
||||||
|
1. Open your crontab editor by running `crontab -e`.
|
||||||
|
2. Add a new line to the file to schedule the job. For example, to run the script **every day at 10:00 PM**, add the following line:
|
||||||
|
|
||||||
|
```
|
||||||
|
0 22 * * * /path/to/your/project/reddit_stock_analyzer/run_daily_job.sh >> /path/to/your/project/reddit_stock_analyzer/cron.log 2>&1
|
||||||
|
```
|
||||||
|
* `0 22 * * *` means at minute 0 of hour 22, every day, every month, every day of the week.
|
||||||
|
* `>> /path/to/your/.../cron.log 2>&1` is highly recommended. It redirects all output (both standard and error) from the script into a log file, so you can check if the job ran successfully.
|
||||||
|
|
||||||
|
Your project is now fully automated to scrape, analyze, visualize, and post data every day.
|
@@ -1,51 +1,73 @@
|
|||||||
# export_image.py
|
# export_image.py
|
||||||
|
|
||||||
import argparse
|
import argparse
|
||||||
from playwright.sync_api import sync_playwright
|
import os
|
||||||
import time
|
import time
|
||||||
|
from playwright.sync_api import sync_playwright
|
||||||
|
|
||||||
def export_subreddit_image(subreddit_name, weekly=False):
|
# Define the output directory as a constant
|
||||||
"""
|
OUTPUT_DIR = "images"
|
||||||
Launches a headless browser to take a screenshot of a subreddit's image view.
|
|
||||||
"""
|
def export_image(url_path, filename_prefix):
|
||||||
view_type = "weekly" if weekly else "daily"
|
"""
|
||||||
print(f"Exporting {view_type} image for r/{subreddit_name}...")
|
Launches a headless browser, navigates to a URL path, and screenshots
|
||||||
|
the .image-container element, saving it to the OUTPUT_DIR.
|
||||||
|
"""
|
||||||
|
print(f"-> Preparing to export image for: {filename_prefix}")
|
||||||
|
|
||||||
|
# 1. Ensure the output directory exists
|
||||||
|
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||||
|
|
||||||
# The URL our Flask app serves
|
|
||||||
base_url = "http://127.0.0.1:5000"
|
base_url = "http://127.0.0.1:5000"
|
||||||
path = f"image/weekly/{subreddit_name}" if weekly else f"image/{subreddit_name}"
|
url = f"{base_url}/{url_path}"
|
||||||
url = f"{base_url}/{path}"
|
|
||||||
|
|
||||||
# Define the output filename
|
# 2. Construct the full output path including the new directory
|
||||||
output_file = f"{subreddit_name}_{'weekly' if weekly else 'daily'}_{int(time.time())}.png"
|
output_file = os.path.join(OUTPUT_DIR, f"{filename_prefix}_{int(time.time())}.png")
|
||||||
|
|
||||||
with sync_playwright() as p:
|
with sync_playwright() as p:
|
||||||
browser = p.chromium.launch()
|
try:
|
||||||
page = browser.new_page()
|
browser = p.chromium.launch()
|
||||||
|
page = browser.new_page()
|
||||||
|
|
||||||
# Set a large viewport for high-quality screenshots
|
page.set_viewport_size({"width": 1920, "height": 1080})
|
||||||
page.set_viewport_size({"width": 1920, "height": 1080})
|
|
||||||
|
|
||||||
print(f"Navigating to {url}...")
|
print(f" Navigating to {url}...")
|
||||||
page.goto(url)
|
page.goto(url, wait_until="networkidle") # Wait for network to be idle
|
||||||
|
|
||||||
# Important: Give the page a second to ensure all styles and fonts have loaded
|
# Target the specific element we want to screenshot
|
||||||
page.wait_for_timeout(1000)
|
element = page.locator(".image-container")
|
||||||
|
|
||||||
# Target the specific element we want to screenshot
|
print(f" Saving screenshot to {output_file}...")
|
||||||
element = page.locator(".image-container")
|
element.screenshot(path=output_file)
|
||||||
|
|
||||||
print(f"Saving screenshot to {output_file}...")
|
browser.close()
|
||||||
element.screenshot(path=output_file)
|
print(f"-> Export complete! Image saved to {output_file}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"\nAn error occurred during export: {e}")
|
||||||
|
print("Please ensure the 'rstat-dashboard' server is running in another terminal.")
|
||||||
|
|
||||||
browser.close()
|
|
||||||
print("Export complete!")
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
|
# Use a mutually exclusive group to ensure only one mode is chosen
|
||||||
parser = argparse.ArgumentParser(description="Export subreddit sentiment images.")
|
parser = argparse.ArgumentParser(description="Export subreddit sentiment images.")
|
||||||
parser.add_argument("subreddit", help="The name of the subreddit to export.")
|
group = parser.add_mutually_exclusive_group(required=True)
|
||||||
parser.add_argument("--weekly", action="store_true", help="Export the weekly view instead of the daily view.")
|
group.add_argument("-s", "--subreddit", help="The name of the subreddit to export.")
|
||||||
|
group.add_argument("-o", "--overall", action="store_true", help="Export the overall summary image.")
|
||||||
|
|
||||||
|
parser.add_argument("-w", "--weekly", action="store_true", help="Export the weekly view instead of the daily view (only for --subreddit).")
|
||||||
args = parser.parse_args()
|
args = parser.parse_args()
|
||||||
|
|
||||||
# NOTE: This script assumes your 'rstat-dashboard' server is already running in another terminal.
|
# Determine the correct URL path and filename based on arguments
|
||||||
export_subreddit_image(args.subreddit, args.weekly)
|
if args.subreddit:
|
||||||
|
view_type = "weekly" if args.weekly else "daily"
|
||||||
|
url_path_to_render = f"image/{view_type}/{args.subreddit}"
|
||||||
|
filename_prefix_to_save = f"{args.subreddit}_{view_type}"
|
||||||
|
export_image(url_path_to_render, filename_prefix_to_save)
|
||||||
|
|
||||||
|
elif args.overall:
|
||||||
|
if args.weekly:
|
||||||
|
print("Warning: --weekly flag has no effect with --overall. Exporting overall summary.")
|
||||||
|
url_path_to_render = "image/overall"
|
||||||
|
filename_prefix_to_save = "overall_summary"
|
||||||
|
export_image(url_path_to_render, filename_prefix_to_save)
|
104
post_to_reddit.py
Normal file
104
post_to_reddit.py
Normal file
@@ -0,0 +1,104 @@
|
|||||||
|
# post_to_reddit.py
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import os
|
||||||
|
import glob
|
||||||
|
from datetime import datetime, timezone
|
||||||
|
import praw
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
|
||||||
|
# --- CONFIGURATION ---
|
||||||
|
IMAGE_DIR = "images"
|
||||||
|
|
||||||
|
def get_reddit_instance():
|
||||||
|
"""Initializes and returns a PRAW Reddit instance from .env credentials."""
|
||||||
|
load_dotenv()
|
||||||
|
client_id = os.getenv("REDDIT_CLIENT_ID")
|
||||||
|
client_secret = os.getenv("REDDIT_CLIENT_SECRET")
|
||||||
|
user_agent = os.getenv("REDDIT_USER_AGENT")
|
||||||
|
username = os.getenv("REDDIT_USERNAME") # <-- Add your Reddit username to .env
|
||||||
|
password = os.getenv("REDDIT_PASSWORD") # <-- Add your Reddit password to .env
|
||||||
|
|
||||||
|
if not all([client_id, client_secret, user_agent, username, password]):
|
||||||
|
print("Error: Reddit API credentials (including username/password) not found in .env file.")
|
||||||
|
return None
|
||||||
|
|
||||||
|
return praw.Reddit(
|
||||||
|
client_id=client_id,
|
||||||
|
client_secret=client_secret,
|
||||||
|
user_agent=user_agent,
|
||||||
|
username=username,
|
||||||
|
password=password
|
||||||
|
)
|
||||||
|
|
||||||
|
def find_latest_image(pattern):
|
||||||
|
"""Finds the most recent file in the IMAGE_DIR that matches a given pattern."""
|
||||||
|
try:
|
||||||
|
search_path = os.path.join(IMAGE_DIR, pattern)
|
||||||
|
list_of_files = glob.glob(search_path)
|
||||||
|
if not list_of_files:
|
||||||
|
return None
|
||||||
|
# The latest file will be the one with the highest modification time
|
||||||
|
latest_file = max(list_of_files, key=os.path.getmtime)
|
||||||
|
return latest_file
|
||||||
|
except Exception as e:
|
||||||
|
print(f"Error finding image file: {e}")
|
||||||
|
return None
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""Main function to find an image and post it to Reddit."""
|
||||||
|
parser = argparse.ArgumentParser(description="Find the latest sentiment image and post it to a subreddit.")
|
||||||
|
parser.add_argument("-s", "--subreddit", help="The source subreddit of the image to post. (Defaults to overall summary)")
|
||||||
|
parser.add_argument("-w", "--weekly", action="store_true", help="Post the weekly summary instead of the daily one.")
|
||||||
|
parser.add_argument("-t", "--target-subreddit", default="rstat", help="The subreddit to post the image to. (Default: rstat)")
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# --- 1. Determine filename pattern and post title ---
|
||||||
|
current_date_str = datetime.now(timezone.utc).strftime("%Y-%m-%d")
|
||||||
|
|
||||||
|
if args.subreddit:
|
||||||
|
view_type = "weekly" if args.weekly else "daily"
|
||||||
|
filename_pattern = f"{args.subreddit.lower()}_{view_type}_*.png"
|
||||||
|
post_title = f"{view_type.capitalize()} Ticker Sentiment for r/{args.subreddit} ({current_date_str})"
|
||||||
|
else:
|
||||||
|
# Default to the overall summary
|
||||||
|
if args.weekly:
|
||||||
|
print("Warning: --weekly flag has no effect for overall summary. Posting overall daily image.")
|
||||||
|
filename_pattern = "overall_summary_*.png"
|
||||||
|
post_title = f"Overall Top 10 Ticker Mentions Across Reddit ({current_date_str})"
|
||||||
|
|
||||||
|
print(f"Searching for image pattern: {filename_pattern}")
|
||||||
|
|
||||||
|
# --- 2. Find the latest image file ---
|
||||||
|
image_to_post = find_latest_image(filename_pattern)
|
||||||
|
|
||||||
|
if not image_to_post:
|
||||||
|
print(f"Error: No image found matching the pattern '{filename_pattern}'. Please run the scraper and exporter first.")
|
||||||
|
return
|
||||||
|
|
||||||
|
print(f"Found image: {image_to_post}")
|
||||||
|
|
||||||
|
# --- 3. Connect to Reddit and submit ---
|
||||||
|
reddit = get_reddit_instance()
|
||||||
|
if not reddit:
|
||||||
|
return
|
||||||
|
|
||||||
|
try:
|
||||||
|
target_sub = reddit.subreddit(args.target_subreddit)
|
||||||
|
print(f"Submitting '{post_title}' to r/{target_sub.display_name}...")
|
||||||
|
|
||||||
|
submission = target_sub.submit_image(
|
||||||
|
title=post_title,
|
||||||
|
image_path=image_to_post,
|
||||||
|
flair_id=None # Optional: You can add a flair ID here if you want
|
||||||
|
)
|
||||||
|
|
||||||
|
print("\n--- Post Successful! ---")
|
||||||
|
print(f"Post URL: {submission.shortlink}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"\nAn error occurred while posting to Reddit: {e}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
@@ -1,14 +1,14 @@
|
|||||||
# rstat_tool/dashboard.py
|
# rstat_tool/dashboard.py
|
||||||
|
|
||||||
from flask import Flask, render_template
|
from flask import Flask, render_template
|
||||||
from datetime import datetime, timedelta
|
from datetime import datetime, timedelta, timezone
|
||||||
from .logger_setup import get_logger
|
from .logger_setup import get_logger
|
||||||
from .database import (
|
from .database import (
|
||||||
get_overall_summary,
|
get_overall_summary,
|
||||||
get_subreddit_summary,
|
get_subreddit_summary,
|
||||||
get_all_scanned_subreddits,
|
get_all_scanned_subreddits,
|
||||||
get_deep_dive_details,
|
get_deep_dive_details,
|
||||||
get_image_view_summary,
|
get_daily_summary_for_subreddit,
|
||||||
get_weekly_summary_for_subreddit,
|
get_weekly_summary_for_subreddit,
|
||||||
get_overall_image_view_summary
|
get_overall_image_view_summary
|
||||||
)
|
)
|
||||||
@@ -55,13 +55,13 @@ def deep_dive(symbol):
|
|||||||
posts = get_deep_dive_details(symbol)
|
posts = get_deep_dive_details(symbol)
|
||||||
return render_template("deep_dive.html", posts=posts, symbol=symbol)
|
return render_template("deep_dive.html", posts=posts, symbol=symbol)
|
||||||
|
|
||||||
@app.route("/image/<name>")
|
@app.route("/image/daily/<name>")
|
||||||
def image_view(name):
|
def daily_image_view(name):
|
||||||
"""The handler for the image-style dashboard."""
|
"""The handler for the image-style dashboard."""
|
||||||
tickers = get_image_view_summary(name)
|
tickers = get_daily_summary_for_subreddit(name)
|
||||||
current_date = datetime.utcnow().strftime("%Y-%m-%d")
|
current_date = datetime.now(timezone.utc).strftime("%Y-%m-%d")
|
||||||
return render_template(
|
return render_template(
|
||||||
"image_view.html",
|
"daily_image_view.html",
|
||||||
tickers=tickers,
|
tickers=tickers,
|
||||||
subreddit_name=name,
|
subreddit_name=name,
|
||||||
current_date=current_date
|
current_date=current_date
|
||||||
@@ -73,7 +73,7 @@ def weekly_image_view(name):
|
|||||||
tickers = get_weekly_summary_for_subreddit(name)
|
tickers = get_weekly_summary_for_subreddit(name)
|
||||||
|
|
||||||
# Create the date range string for the title
|
# Create the date range string for the title
|
||||||
end_date = datetime.utcnow()
|
end_date = datetime.now(timezone.utc)
|
||||||
start_date = end_date - timedelta(days=7)
|
start_date = end_date - timedelta(days=7)
|
||||||
date_range_str = f"{start_date.strftime('%b %d')} - {end_date.strftime('%b %d, %Y')}"
|
date_range_str = f"{start_date.strftime('%b %d')} - {end_date.strftime('%b %d, %Y')}"
|
||||||
|
|
||||||
@@ -88,7 +88,7 @@ def weekly_image_view(name):
|
|||||||
def overall_image_view():
|
def overall_image_view():
|
||||||
"""The handler for the overall image-style dashboard."""
|
"""The handler for the overall image-style dashboard."""
|
||||||
tickers = get_overall_image_view_summary()
|
tickers = get_overall_image_view_summary()
|
||||||
current_date = datetime.utcnow().strftime("%Y-%m-%d")
|
current_date = datetime.now(timezone.utc).strftime("%Y-%m-%d")
|
||||||
return render_template(
|
return render_template(
|
||||||
"overall_image_view.html",
|
"overall_image_view.html",
|
||||||
tickers=tickers,
|
tickers=tickers,
|
||||||
|
@@ -4,7 +4,7 @@ import sqlite3
|
|||||||
import time
|
import time
|
||||||
from .ticker_extractor import COMMON_WORDS_BLACKLIST
|
from .ticker_extractor import COMMON_WORDS_BLACKLIST
|
||||||
from .logger_setup import get_logger
|
from .logger_setup import get_logger
|
||||||
from datetime import datetime, timedelta
|
from datetime import datetime, timedelta, timezone
|
||||||
|
|
||||||
DB_FILE = "reddit_stocks.db"
|
DB_FILE = "reddit_stocks.db"
|
||||||
log = get_logger()
|
log = get_logger()
|
||||||
@@ -276,13 +276,6 @@ def generate_summary_report(limit=20):
|
|||||||
)
|
)
|
||||||
conn.close()
|
conn.close()
|
||||||
|
|
||||||
def get_all_scanned_subreddits():
|
|
||||||
"""Gets a unique list of all subreddits we have data for."""
|
|
||||||
conn = get_db_connection()
|
|
||||||
results = conn.execute("SELECT DISTINCT name FROM subreddits ORDER BY name ASC;").fetchall()
|
|
||||||
conn.close()
|
|
||||||
return [row['name'] for row in results]
|
|
||||||
|
|
||||||
def add_or_update_post_analysis(conn, post_data):
|
def add_or_update_post_analysis(conn, post_data):
|
||||||
"""
|
"""
|
||||||
Inserts a new post analysis record or updates an existing one.
|
Inserts a new post analysis record or updates an existing one.
|
||||||
@@ -302,35 +295,15 @@ def add_or_update_post_analysis(conn, post_data):
|
|||||||
)
|
)
|
||||||
conn.commit()
|
conn.commit()
|
||||||
|
|
||||||
def get_deep_dive_details(ticker_symbol):
|
|
||||||
"""
|
|
||||||
Gets all analyzed posts that mention a specific ticker.
|
|
||||||
"""
|
|
||||||
conn = get_db_connection()
|
|
||||||
query = """
|
|
||||||
SELECT DISTINCT p.*, s.name as subreddit_name FROM posts p
|
|
||||||
JOIN mentions m ON p.post_id = m.post_id
|
|
||||||
JOIN tickers t ON m.ticker_id = t.id
|
|
||||||
JOIN subreddits s ON p.subreddit_id = s.id
|
|
||||||
WHERE LOWER(t.symbol) = LOWER(?)
|
|
||||||
ORDER BY p.post_timestamp DESC;
|
|
||||||
"""
|
|
||||||
results = conn.execute(query, (ticker_symbol,)).fetchall()
|
|
||||||
conn.close()
|
|
||||||
return results
|
|
||||||
|
|
||||||
def get_overall_summary(limit=50):
|
def get_overall_summary(limit=50):
|
||||||
conn = get_db_connection()
|
conn = get_db_connection()
|
||||||
query = """
|
query = """
|
||||||
SELECT
|
SELECT t.symbol, t.market_cap, t.closing_price, COUNT(m.id) as mention_count,
|
||||||
t.symbol, t.market_cap, t.closing_price,
|
|
||||||
COUNT(m.id) as mention_count,
|
|
||||||
SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
|
SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
|
||||||
SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
|
SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
|
||||||
SUM(CASE WHEN m.mention_sentiment BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
|
SUM(CASE WHEN m.mention_sentiment BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
|
||||||
FROM mentions m JOIN tickers t ON m.ticker_id = t.id
|
FROM mentions m JOIN tickers t ON m.ticker_id = t.id
|
||||||
GROUP BY t.symbol, t.market_cap, t.closing_price
|
GROUP BY t.symbol, t.market_cap, t.closing_price ORDER BY mention_count DESC LIMIT ?;
|
||||||
ORDER BY mention_count DESC LIMIT ?;
|
|
||||||
"""
|
"""
|
||||||
results = conn.execute(query, (limit,)).fetchall()
|
results = conn.execute(query, (limit,)).fetchall()
|
||||||
conn.close()
|
conn.close()
|
||||||
@@ -339,86 +312,87 @@ def get_overall_summary(limit=50):
|
|||||||
def get_subreddit_summary(subreddit_name, limit=50):
|
def get_subreddit_summary(subreddit_name, limit=50):
|
||||||
conn = get_db_connection()
|
conn = get_db_connection()
|
||||||
query = """
|
query = """
|
||||||
SELECT
|
SELECT t.symbol, t.market_cap, t.closing_price, COUNT(m.id) as mention_count,
|
||||||
t.symbol, t.market_cap, t.closing_price,
|
|
||||||
COUNT(m.id) as mention_count,
|
|
||||||
SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
|
SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
|
||||||
SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
|
SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
|
||||||
SUM(CASE WHEN m.mention_sentiment BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
|
SUM(CASE WHEN m.mention_sentiment BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
|
||||||
FROM mentions m
|
FROM mentions m JOIN tickers t ON m.ticker_id = t.id JOIN subreddits s ON m.subreddit_id = s.id
|
||||||
JOIN tickers t ON m.ticker_id = t.id
|
WHERE LOWER(s.name) = LOWER(?) GROUP BY t.symbol, t.market_cap, t.closing_price ORDER BY mention_count DESC LIMIT ?;
|
||||||
JOIN subreddits s ON m.subreddit_id = s.id
|
|
||||||
WHERE LOWER(s.name) = LOWER(?)
|
|
||||||
GROUP BY t.symbol, t.market_cap, t.closing_price
|
|
||||||
ORDER BY mention_count DESC LIMIT ?;
|
|
||||||
"""
|
"""
|
||||||
results = conn.execute(query, (subreddit_name, limit)).fetchall()
|
results = conn.execute(query, (subreddit_name, limit)).fetchall()
|
||||||
conn.close()
|
conn.close()
|
||||||
return results
|
return results
|
||||||
|
|
||||||
def get_image_view_summary(subreddit_name):
|
def get_daily_summary_for_subreddit(subreddit_name):
|
||||||
|
""" Gets a summary for the DAILY image view (last 24 hours). """
|
||||||
conn = get_db_connection()
|
conn = get_db_connection()
|
||||||
|
one_day_ago = datetime.now(timezone.utc) - timedelta(days=1)
|
||||||
|
one_day_ago_timestamp = int(one_day_ago.timestamp())
|
||||||
query = """
|
query = """
|
||||||
SELECT
|
SELECT t.symbol,
|
||||||
t.symbol,
|
|
||||||
COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
|
COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
|
||||||
COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
|
COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
|
||||||
COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
|
COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
|
||||||
COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
|
COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
|
||||||
FROM mentions m
|
FROM mentions m JOIN tickers t ON m.ticker_id = t.id JOIN subreddits s ON m.subreddit_id = s.id
|
||||||
JOIN tickers t ON m.ticker_id = t.id
|
WHERE LOWER(s.name) = LOWER(?) AND m.mention_timestamp >= ?
|
||||||
JOIN subreddits s ON m.subreddit_id = s.id
|
GROUP BY t.symbol ORDER BY (post_mentions + comment_mentions) DESC LIMIT 10;
|
||||||
WHERE LOWER(s.name) = LOWER(?)
|
|
||||||
GROUP BY t.symbol
|
|
||||||
ORDER BY (post_mentions + comment_mentions) DESC
|
|
||||||
LIMIT 10;
|
|
||||||
"""
|
"""
|
||||||
results = conn.execute(query, (subreddit_name,)).fetchall()
|
results = conn.execute(query, (subreddit_name, one_day_ago_timestamp)).fetchall()
|
||||||
conn.close()
|
conn.close()
|
||||||
return results
|
return results
|
||||||
|
|
||||||
def get_weekly_summary_for_subreddit(subreddit_name):
|
def get_weekly_summary_for_subreddit(subreddit_name):
|
||||||
|
""" Gets a summary for the WEEKLY image view (last 7 days). """
|
||||||
conn = get_db_connection()
|
conn = get_db_connection()
|
||||||
seven_days_ago = datetime.utcnow() - timedelta(days=7)
|
seven_days_ago = datetime.now(timezone.utc) - timedelta(days=7)
|
||||||
seven_days_ago_timestamp = int(seven_days_ago.timestamp())
|
seven_days_ago_timestamp = int(seven_days_ago.timestamp())
|
||||||
query = """
|
query = """
|
||||||
SELECT
|
SELECT t.symbol,
|
||||||
t.symbol,
|
|
||||||
COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
|
COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
|
||||||
COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
|
COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
|
||||||
COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
|
COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
|
||||||
COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
|
COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
|
||||||
FROM mentions m
|
FROM mentions m JOIN tickers t ON m.ticker_id = t.id JOIN subreddits s ON m.subreddit_id = s.id
|
||||||
JOIN tickers t ON m.ticker_id = t.id
|
|
||||||
JOIN subreddits s ON m.subreddit_id = s.id
|
|
||||||
WHERE LOWER(s.name) = LOWER(?) AND m.mention_timestamp >= ?
|
WHERE LOWER(s.name) = LOWER(?) AND m.mention_timestamp >= ?
|
||||||
GROUP BY t.symbol
|
GROUP BY t.symbol ORDER BY (post_mentions + comment_mentions) DESC LIMIT 10;
|
||||||
ORDER BY (post_mentions + comment_mentions) DESC
|
|
||||||
LIMIT 10;
|
|
||||||
"""
|
"""
|
||||||
results = conn.execute(query, (subreddit_name, seven_days_ago_timestamp)).fetchall()
|
results = conn.execute(query, (subreddit_name, seven_days_ago_timestamp)).fetchall()
|
||||||
conn.close()
|
conn.close()
|
||||||
return results
|
return results
|
||||||
|
|
||||||
def get_overall_image_view_summary():
|
def get_overall_image_view_summary():
|
||||||
"""
|
""" Gets a summary of top tickers across ALL subreddits for the image view. """
|
||||||
Gets a summary of top tickers across ALL subreddits for the image view.
|
|
||||||
"""
|
|
||||||
conn = get_db_connection()
|
conn = get_db_connection()
|
||||||
query = """
|
query = """
|
||||||
SELECT
|
SELECT t.symbol,
|
||||||
t.symbol,
|
|
||||||
COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
|
COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
|
||||||
COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
|
COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
|
||||||
COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
|
COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
|
||||||
COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
|
COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
|
||||||
FROM mentions m
|
FROM mentions m JOIN tickers t ON m.ticker_id = t.id
|
||||||
JOIN tickers t ON m.ticker_id = t.id
|
GROUP BY t.symbol ORDER BY (post_mentions + comment_mentions) DESC LIMIT 10;
|
||||||
-- No JOIN or WHERE for subreddit, as we want all of them
|
|
||||||
GROUP BY t.symbol
|
|
||||||
ORDER BY (post_mentions + comment_mentions) DESC
|
|
||||||
LIMIT 10;
|
|
||||||
"""
|
"""
|
||||||
results = conn.execute(query).fetchall()
|
results = conn.execute(query).fetchall()
|
||||||
conn.close()
|
conn.close()
|
||||||
return results
|
return results
|
||||||
|
|
||||||
|
def get_deep_dive_details(ticker_symbol):
|
||||||
|
""" Gets all analyzed posts that mention a specific ticker. """
|
||||||
|
conn = get_db_connection()
|
||||||
|
query = """
|
||||||
|
SELECT DISTINCT p.*, s.name as subreddit_name FROM posts p
|
||||||
|
JOIN mentions m ON p.post_id = m.post_id JOIN tickers t ON m.ticker_id = t.id
|
||||||
|
JOIN subreddits s ON p.subreddit_id = s.id
|
||||||
|
WHERE LOWER(t.symbol) = LOWER(?) ORDER BY p.post_timestamp DESC;
|
||||||
|
"""
|
||||||
|
results = conn.execute(query, (ticker_symbol,)).fetchall()
|
||||||
|
conn.close()
|
||||||
|
return results
|
||||||
|
|
||||||
|
def get_all_scanned_subreddits():
|
||||||
|
""" Gets a unique list of all subreddits we have data for. """
|
||||||
|
conn = get_db_connection()
|
||||||
|
results = conn.execute("SELECT DISTINCT name FROM subreddits ORDER BY name ASC;").fetchall()
|
||||||
|
conn.close()
|
||||||
|
return [row['name'] for row in results]
|
@@ -5,7 +5,7 @@
|
|||||||
{% block content %}
|
{% block content %}
|
||||||
<h1>
|
<h1>
|
||||||
Top 10 Tickers in r/{{ subreddit_name }}
|
Top 10 Tickers in r/{{ subreddit_name }}
|
||||||
<a href="/image/{{ subreddit_name }}" target="_blank" style="font-size: 0.8rem; margin-left: 1rem; font-weight: normal;">(View Daily Image)</a>
|
<a href="/image/daily/{{ subreddit_name }}" target="_blank" style="font-size: 0.8rem; margin-left: 1rem; font-weight: normal;">(View Daily Image)</a>
|
||||||
<!-- ADD THIS NEW LINK -->
|
<!-- ADD THIS NEW LINK -->
|
||||||
<a href="/image/weekly/{{ subreddit_name }}" target="_blank" style="font-size: 0.8rem; margin-left: 1rem; font-weight: normal;">(View Weekly Image)</a>
|
<a href="/image/weekly/{{ subreddit_name }}" target="_blank" style="font-size: 0.8rem; margin-left: 1rem; font-weight: normal;">(View Weekly Image)</a>
|
||||||
</h1>
|
</h1>
|
||||||
|
Reference in New Issue
Block a user