Video summary

I Built a Bot to Trade Based on Reddit Posts

Main summary

Key takeaways

Finance

Finance-focused summary (markets/investing/portfolio/risk)

  • The creator attempts to build an automated trading system using WallStreetBets (WSB) Reddit posts to predict when stocks will move “before they take off.”
  • The core research question is whether “wisdom of crowds” from WSB can beat the market, using the S&P 500 as the benchmark.
  • Multiple trading signals are tested, and all fail to outperform the index. Some approaches also produce very large drawdowns.

Instruments / tickers / assets mentioned

  • S&P 500: baseline index for comparison
  • GameStop: explicitly referenced, linked to the short squeeze dynamic

(No other specific tickers are explicitly named in the subtitles.)

Methodology / step-by-step frameworks tested

1) Naive “buy what’s mentioned”

  • Scrape WSB posts in real time
  • Scan each post for stock tickers
  • Count mentions per ticker each day
  • Run a randomized baseline:
    • Monte Carlo: randomly select thousands of posts
    • Buy the stock mentioned in each selected post
    • Hold for 60 days, then sell
    • Compare returns to buying the S&P 500 over the same day/period
  • Key feature: no sentiment selection (posts may be bullish or bearish)

2) “Herd-following” via most-mentioned

  • For a daily window (2021 to 2022):
    • Count ticker mentions daily
    • Identify the single most mentioned stock each day
    • Buy it and hold until it stops being the most mentioned
  • Rationale: people follow attention/hype

3) “Velocity” / acceleration of mentions

  • Compute mention growth rate (“velocity”):
    • Example: 5 mentions Monday → 10 Tuesday → 15 Wednesday
    • Velocity defined as increase in mentions per day (incremental mentions)
  • Daily routine:
    • Rank tickers by velocity each day
    • Buy the fastest riser
    • Hold and then sell when the signal changes (described as “buy fastest riser then hold and sell”)
  • Outcome: reported to be poor versus the index

4) Normalized velocity to remove subreddit growth

  • Problem addressed: raw mention increases may reflect WSB growing overall
  • Fix:
    • Count ticker mentions as a fraction of total posts that day
    • Equivalent to dividing by a 7-day rolling average of total posts (per a referenced Oxford paper)
  • Still fails to beat the market

5) Text sentiment classification (Oxford approach, referenced)

  • Oxford researchers:
    • Scraped WSB posts 2012–2022
    • Used AI to classify each post as bullish vs bearish
    • Simulated trading and evaluated predictive power
  • Manual labeling:
    • 4,000 posts manually classified to train the model

Key numbers (performance metrics, timelines, explicit results)

Monte Carlo / random post picks

  • Median return (WSB random picks): -10.3% over ~2 months
  • Median return (S&P 500): +4.6% over ~2 months

Most-mentioned strategy

  • Time window: 2021 to 2022
  • Performance described qualitatively as driven by GameStop short squeeze
  • Fails because the stock stays top-mentioned even after peak popularity → profits largely revert

Velocity strategy

  • Trades executed: 222 trades over ~1 year
  • Average strategy return: -1.1%
  • Average S&P 500 return over the same period: +30%
  • Best trade: +58%
  • Worst trade: -83%
  • Interpretation implied by results: severe volatility and poor risk-adjusted outcomes (very large tail losses)

Normalized velocity strategy

  • S&P 500 return over the time window: +30%
  • New strategy return: -0.2% over ~1 year
  • Note: described as “better than before,” but still far below the index

Oxford sentiment model (referenced results)

  • Sentiment prediction accuracy: 69%
  • Trading simulation using sentiment:
    • General case next-day loss: -2.8%
    • During “GameStop year”: -4.4%
  • Nuance: when isolating “due diligence” / researched writeups, returns reportedly flipped positive, though the subtitles don’t provide the exact magnitude for that subset

Recommendations / cautions explicitly implied

  • The creator concludes that:
    • Mention count / velocity / hype-based signals do not beat the index.
    • Attention metrics are confounded by factors like overall subreddit growth and “front-page/news” effects (i.e., by the time it’s most mentioned, everyone already knows).
  • Risk reality check from results:
    • Even when wins occur (e.g., +58%), losses can be extreme (-83%), suggesting inadequate drawdown control.

Disclosures / disclaimers

  • No explicit “not financial advice” wording appears in the provided subtitles.
  • Results are framed as backtests/experiments, not guaranteed investing success.

Presenters / sources mentioned

  • Oxford University researchers: referenced paper; scraping 2012–2022 and manual labeling of 4,000 posts
  • Video creator / narrator: implied by statements like “I built… I ran… I post…” (name not provided in subtitles)
  • S&P 500: benchmark/index (not a presenter)
  • WallStreetBets (WSB): data source (not a presenter)

Original video