Reddit-Based Stock Price Prediction

@ZohaibRoy

⭐⭐⭐⭐⭐ Create Actionable Equity Market Forecasts from Reddit Chatter ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a solution to turn Reddit chatter into market forecasts. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for financial data analysis. I will pull and analyze Reddit data using its API, rank tickers by trading volume, and combine this with historical market data to create actionable insights. ➡️ Why Me? I can easily do your project to analyze Reddit chatter and predict market trends as I have 5 years of experience in data analysis, machine learning, and Python programming. My expertise includes data gathering, sentiment analysis, and model evaluation. I also have a strong grip on data visualization and statistical methods, ensuring a comprehensive approach to your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Python Programming ✅ Data Analysis ✅ Machine Learning ✅ Sentiment Analysis ✅ API Integration ✅ Data Visualization ✅ Statistical Modeling ✅ Data Storage (Parquet, CSV) ✅ OHLCV Data Retrieval ✅ Performance Metrics Calculation ✅ Project Documentation ✅ Reproducible Environments Waiting for your response! Best Regards, Zohaib

@nrajuu07

As an experienced data scientist, I can confidently say that I am the ideal candidate for your Reddit-based stock price prediction project. My proficiencies span numerous areas that are critical to completing this task successfully. From conducting statistical and quantitative analysis to dimensional reduction and predictive modeling, I can assure you of an end-to-end solution. Moreover, I am well-versed in using Python and other essential tools like TensorFlow, Pandas, NumPy, and Scikit-learn which will enable me to fulfill the demands of this project within stipulated timeframes.

@usamae

I do Python data work and ML regularly, and this scope is straightforward - PRAW for Reddit data collection, OHLCV from Yahoo Finance via yfinance, sentiment scoring per-day per-ticker, then train and compare two models (I would go LSTM and XGBoost as a natural pair for this kind of time-series/NLP problem). All code will be in reproducible notebooks with requirements.txt. Volume-based top-10 selection, 3-5 year date range, proper evaluation with MAE/RMSE/directional accuracy. Ready to start right away. - Usama

@qualityxenter

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@MQamar123

Being a seasoned software developer with over 7 years of experience, I'll bring not only my extensive Python repertoire to your project but also my deep understanding of web development and data handling. This project demands proficiency in the comprehensive use of Python combined with AI, something I have excelled at throughout my career. My repertoire also includes frameworks such as Django which might be useful given this project's scope. Furthermore, my experience in working with APIs (like the Reddit API) and querying large datasets will enable me to efficiently handle and analyze the extensive amount of data you're aiming to pull from Reddit and Yahoo Finance. Not only I ensure that all posts and comments containing relevant information on publicly traded tickers are adequately gathered, but I'll also supply clear notebooks/scripts that document the process thoroughly for reproducibility. Finally, I am known for prioritizing client satisfaction above all else. Your key criteria for this project; successful end-to-end code deployment and continuous dataset spanning 3-5 years, are in safe hands with me. With meticulous attention to detail, I guarantee that the top 10 selection for the model will be based solely on trading-volume ranking as expected. All in all, not only am I equipped with the skills and experience necessary for this venture, but I'm truly motivated to make it thrive. Let's redefine LSTM!

@jesica46

Hello, I reviewed your Reddit-Based Stock Price Prediction project and I’m confident I can complete this Python task efficiently and according to your requirements. I have hands-on experience in Python development, including automation scripts, data processing tools, and problem-solving solutions. My focus is on writing clean, well-structured, and reliable code that is easy to maintain and performs efficiently. For your project, I will: ✔ Understand the requirements carefully ✔ Develop clean and structured Python code ✔ Test the solution and handle possible errors ✔ Optimize the script for accuracy and performance ✔ Deliver the work within the agreed deadline Please feel free to share more details about the project so I can review everything and start working right away. Best regards

@Hemantt841

Your project to transform Reddit chatter into actionable stock forecasts is fascinating, and I see you need a robust pipeline from data assembly through modeling and evaluation. You want a system that pulls Reddit posts and comments mentioning tickers over several years, ranks them by trading volume, and combines sentiment with market data to predict next-day prices. I understand you require pulling data via Reddit’s API using PRAW or Pushshift, filtering to the top ten tickers by daily trading volume, and storing clean datasets with sentiment scores. Then, you want historical OHLCV data from Yahoo Finance paired with this sentiment data, followed by training two machine-learning models in Python with thorough performance evaluation and reproducible environments. I have built similar end-to-end pipelines that integrate Reddit data scraping with sentiment analysis and stock price prediction using Python, PRAW, and machine learning libraries like scikit-learn. I’ve developed reproducible notebooks that align social sentiment with market data and compared model metrics such as MAE and RMSE to guide investment insights, ensuring clean, queryable datasets and automated workflows. I can complete this project within 10 days, delivering fully documented code and reproducible environments for your review. Let’s discuss the details to ensure it meets your expectations precisely.

@Algokage

Hello, I can efficiently deliver the Reddit-based stock price prediction project by assembling data from Reddit’s API, filtering top tickers by trading volume, and storing clean, queryable datasets. I’ll fetch historical OHLCV data from Yahoo Finance, train two machine-learning models for next-day price prediction, and evaluate their performance with MAE, RMSE, and directional accuracy. All code will be reproducible with Python notebooks and environment files. With 5+ years of experience, I ensure end-to-end automation and adherence to your acceptance criteria. Message me for samples or to discuss further. Thanks, Adegoke. M

@utsav007pandya

Hello. I can build an end-to-end Python pipeline that transforms long-term Reddit chatter into actionable equity market forecasts. The system will pull posts and comments mentioning tickers over the past 3–5 years via Reddit’s API, rank the top ten by trading volume, and store raw text with sentiment scores in a clean, queryable format. Historical OHLCV data will be paired for each ticker, and I’ll train two machine-learning models to predict next-day price movements, providing clear performance comparisons (MAE, RMSE, directional accuracy). Full Python notebooks, scripts, and reproducible environments will be delivered, ready to run without manual tweaks. Portfolio: https://www.freelancer.com/u/utsav007pandya

@mudassirs52

✔ I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram Reddit Data Collection ⟶⟶ Ticker Extraction & Filtering ⟶⟶ Sentiment Analysis Processing ⟶⟶ Dataset Structuring (Parquet/CSV) ⟶⟶ Yahoo Finance Market Data Integration ⟶⟶ Feature Engineering ⟶⟶ ML Model Training (2 Models) ⟶⟶ Performance Evaluation ⟶⟶ Documentation & Reproducible Environment Key Highlights ✔ Reddit data mining — collect posts and comments mentioning publicly traded tickers over the past 3–5 years using Reddit APIs such as PRAW or Pushshift. ✔ Ticker filtering system — extract ticker mentions and rank companies based on daily trading volume, selecting the top 10 companies for the study. ✔ Structured dataset — store raw Reddit text, timestamps, metadata, and calculated sentiment scores in a clean dataset using Parquet or CSV format for efficient querying. ✔ Market data integration — fetch historical OHLCV data from Yahoo Finance and align it with Reddit sentiment features by trading date. ✔ Feature engineering — generate sentiment-based indicators such as daily sentiment averages, post volume, and engagement metrics. ✔ Machine learning models — train two different predictive models (e.g., Random Forest, XGBoost, or LSTM) to forecast next-day closing price or percentage movement. ✔ Model evaluation — compare model performance using MAE, RMSE, and directional accuracy with clear analysis of strengths and limitations. Best Regards, Asad Data Scientist | Machine Learning | Financial Data Analytics

@johnsonb40

Hello, I am very interested in your project on Reddit-Based Stock Price Prediction. I have experience working with data analysis and machine learning techniques, and I understand how social media sentiment can influence financial markets. I can help collect Reddit data, perform sentiment analysis, and build predictive models that analyze how discussions and trends may correlate with stock price movements. I am comfortable working with tools such as Python, data visualization libraries, and machine learning frameworks to develop reliable models and insights. I focus on building clear, well-documented solutions and delivering accurate results. I would be happy to discuss your dataset, preferred tools, and project goals in more detailto ensure a smooth collaboration. Additionally, I've worked extensively with APIs like PRAW and Pushshift in the past, making me well-equipped to pull the necessary Reddit data based on your specifications and use Yahoo Finance's API to pair it with historical OHLCV data. In terms of data organization, I have prior experience cleaning and storing vast amounts of data in formats like Parquet and CSV for efficient querying. I also completely understand the significance of selecting the top-10 companies based on trading volume rankings for accurate analysis. My meticulous approach will ensure that your project satisfies all acceptance criteria, from the end-to-end running of code to maintaining a continuous 3-5 year-span dataset.

@mfilipovacfl

Hi, I see you want to turn long-term Reddit chatter into actionable forecasts for the stock market, pulling historical posts/comments, pairing them with OHLCV data, and training models to predict next-day price movements. I’ve worked on projects combining social media sentiment with financial data using Python. I’ve collected Reddit data via PRAW, aligned it with historical stock prices, and built predictive models with clear evaluation metrics. All scripts were fully reproducible, with datasets stored in CSV or Parquet for easy querying. For your project, I’d start by assembling the Reddit dataset, filtering by trading volume to identify top 10 tickers, then pair the data with Yahoo Finance OHLCV. From there, I’d train two models, compare performance using MAE, RMSE, and directional accuracy, and deliver well-documented, reproducible code. If you like, we can set up a quick call to confirm your preferred top-10 selection method and any specifics on model evaluation. Best regards, Mihailo

@skolarai

I noticed you're pulling historical Reddit data but didn't mention how you're handling Pushshift's recent API constraints — worth clarifying if you're routing through an alternative archive or just PRAW's current data limits. Built a similar sentiment-to-signal pipeline for crypto trading last quarter, so the Reddit→ticker→forecast flow is familiar territory. What's your confidence threshold for triggering a trade signal, and are you weighting recent sentiment more heavily than older posts.

@steffank2

Hello, I have completed similar projects outside of Freelancer, recently helping a client transform social media data into actionable stock market insights, improving their trading strategies significantly. I understand you need a clean, professional, and user-friendly pipeline that seamlessly integrates Reddit data with market data, automating the entire process from data assembly to modeling and evaluation, ensuring top-ten tickers are ranked by daily trading volume. With my expertise in Python, data engineering, and machine learning, I will build and document a reliable end-to-end system that pulls and processes Reddit posts using PRAW or Pushshift, fetches Yahoo Finance data, and delivers reproducible predictive models. I am doing it at a discounted price because I want good reviews instead of a lot of money, I have tons of experience and have done other projects off site. I would love to chat more about your project! Regards, Steffan Koekemoer

@wirework

Hi, this project caught my eye. Reddit sentiment combined with stock prediction is a great use case that I have hands-on experience with. For data collection, I use PRAW (Python Reddit API Wrapper) to pull posts and comments from target subreddits like r/wallstreetbets, r/stocks, r/investing. For sentiment analysis, I have been building with GPT-5 and the new GPT-5.4 with its 1M token context is perfect here, we can feed entire comment threads in one pass and get nuanced sentiment scoring that goes way beyond simple positive/negative classification. For the ML prediction layer, I will use XGBoost 3.2 (latest release, Feb 2026) which just added improved categorical feature support with automatic re-coding, meaning our sentiment categories and ticker labels get handled natively without manual encoding. LightGBM 4.6 is another option with its leaf-wise growth strategy that trains faster on high-dimensional feature sets. Both are still the gold standard for tabular ML, consistently beating deep learning on structured data. Features I will engineer: sentiment scores (granular, not just binary), post volume and velocity, comment-to-upvote ratios, momentum indicators from price data, and cross-subreddit signal aggregation. Walk-forward validation so the model never sees future data during training. Deliverable: clean Python pipeline with Reddit scraping, feature engineering, model training with proper evaluation metrics (Sharpe ratio, directional accuracy, not just RMSE), and prediction output. Can add a simple dashboard if useful. Can have the core pipeline done in 2-3 days. Pricing competitively to build up reviews.

@AllanMalcomM

the part most people will underestimate on this is the ticker extraction. Reddit text is messy — "$TSLA" is a ticker but "$20 I found on the ground" is not. "AAPL" might be a ticker or someone misspelling apple. without a solid extraction step, everything downstream is garbage. i'd use a whitelist of actively traded tickers cross-referenced against daily volume, plus cashtag regex ($XXXX format). deduplicate per user per day so one person spamming "buy GME" 50 times doesn't skew the signal. the full pipeline: - data: PRAW for recent Reddit API + Pushshift for 3-5yr historical. filter to r/wallstreetbets, r/stocks, r/investing, r/options - sentiment: VADER as baseline, fine-tuned classifier for Reddit finance language - features: sentiment momentum (3-day rolling), mention velocity, volume-weighted scores, cross-subreddit agreement - model: walk-forward validation (not random split — crucial for time series). XGBoost baseline, LSTM if data supports it - output: Jupyter notebook + clean CSV + README so you can rerun and tweak data pipeline working in 4 days. full model + docs in 7. one question: are you looking for price targets or directional predictions (up/down with confidence)? different modeling approach for each.

@mrbhautik

With my in-depth understanding of both data analysis and machine learning, I am confident in my capabilities to successfully undertake this Reddit-Based Stock Price Prediction project. My proficiency in Python, Pandas, and NumPy will ensure that your datasets are efficiently assembled, analyzed, and visualized for optimal modeling and evaluation. Having previously developed AI-powered applications such as chatbots and workflow automation systems, I understand the essence of clean, scalable, production-ready code and will deliver this for your project. In terms of the Acceptance Criteria, my approach guarantees an error-free end-to-end system that runs seamlessly without any manual tweaking. Moreover, my expertise in scraping data using different APIs amplifies my ability to build an extensive dataset covering a specific 3-5 year span with precision. I assure you that my output will demonstrate actualization of this criterion with clarity. Working together on this promising project would be a great pleasure as it presents an ideal opportunity to merge your vision and potential immense value with my skills and expertise. With high-quality documentation and reproducible environments for needed scripts or notebooks in Python as part of my service delivery package, rest assured that you will have full control over all aspects of the project at every stage. Let's turn Reddits' chatter into cash-churning forecasts!

@rabiacnsl

Hello, I’m Rabia, a computer engineer. I have been working with Python for two years, focusing on machine learning, deep learning, and preprocessing (image and signal processing). I have developed machine learning models that detect anemia from the hand, eye, and nails using image processing. I have also developed machine learning and deep learning models for EEG-based lie detection using signal processing.

@Mabena1

Hello, I’m Mpumelelo Mabena. I’m confident I can deliver a clean, professional, and fully automated pipeline to transform Reddit chatter into actionable equity forecasts. My skill set positions me well to execute this successfully. I see you need a seamless flow for data assembly, market referencing, and predictive modeling, with integrated sentiment scores aligned to trading volumes. With expertise in AI automation, web/app development, and digital solutions, I’ll build scalable, user-friendly code that runs end-to-end and meets your acceptance criteria. While I am new to Freelancer, I have strong real-world experience and have completed multiple successful projects off the platform. Could you share your timeline and priority areas for initial focus?