By name: trading-strategy-explorer description: Analyzes a Twitter/X profile to extract equity and options trading strategies and alpha ideas. Accepts a Twitter handle or profile URL, downloads the tweet archive using tweety-ns, and produces a structured markdown report of strategies found.
Trading Strategy Explorer
Analyze a Twitter/X profile for equity and equity options trading strategies, alpha ideas, and market edge concepts.
Input
The user will provide a Twitter/X handle or profile URL. Extract the bare username (strip @, strip URL prefix).
Examples:
@OptionsHawk→OptionsHawkhttps://x.com/OptionsHawk→OptionsHawktwitter.com/OptionsHawk→OptionsHawk
Workflow
Make a todo list with all the steps below and work through them one at a time.
Step 1 — Locate the download script
Find the trading_explorer.py script. It should be at:
scripts/trading_explorer.pyrelative to the current working directory, OR- Search for it with Glob if not found there
If the script is missing, tell the user and stop.
Step 2 — Check Python dependencies
Run:
python3 -c "from tweety import TwitterAsync; print('ok')"
If it fails, run pip install tweety-ns and retry. Stop if it still fails after install.
Step 3 — Check / run the download
Determine the archive directory. The script places archives at:
{repo_root}/{handle}-trading-archive/tweets.json
Where {repo_root} is the parent directory of the scripts/ folder.
If the archive already exists:
- Show the user: how many tweets are archived, when it was last updated.
- Ask if they want to update the archive (fetch newer tweets) or skip straight to analysis.
If no archive exists:
- Inform the user this is a fresh download and it may take a while.
- Proceed with download.
To download / update, run:
python3 {path_to_script} download @{handle} --session trading_explorer --wait 3
Run this command and tail the output. It will print progress page by page. Let it run to completion — it's designed to be long-running.
After the command finishes, confirm the archive was created/updated by checking the JSON file.
Step 4 — Read the archive
Read {archive_dir}/tweets.json.
The structure is:
{
"username": "handle",
"last_updated": "ISO timestamp",
"total_tweets": 1234,
"tweets": [
{
"id": "...",
"date": "...",
"text": "...",
"is_reply": false,
"is_retweet": false,
"likes": 42,
"retweet_counts": 10,
"symbols": ["AAPL", "SPY"],
"hashtags": ["options", "earnings"],
"media": [{"type": "photo", "url": "..."}],
"media_local_paths": ["media/12345_0_image.jpg"],
"urls": [{"expanded_url": "..."}],
"thread_context": [
{ "text": "parent tweet text...", "author_username": "someone_else" }
]
}
]
}
Important notes on reading:
- The JSON file may be large. If it exceeds ~2000 lines, read it in chunks using the
offsetandlimitparameters of the Read tool. - Prioritise tweets with high engagement (likes + retweets > 10) and those that contain
symbols(ticker mentions). - For reply tweets, the
thread_contextarray contains the parent tweet(s) — read these to understand what the user was responding to.
Step 5 — Analyse for trading strategies (equity & options focus)
Read through the tweets carefully. Ignore any non-equity/options content (crypto, macro commentary with no actionable idea, personal life, etc.).
Look for the following categories:
A. Repeating setups / trade ideas
- Specific entry/exit criteria
- Technical levels, chart patterns, or conditions
- Catalyst-driven plays (earnings, FDA dates, macro events)
B. Options-specific strategies
- Defined-risk spreads (call spreads, put spreads, iron condors, etc.)
- Directional options plays (outright calls/puts, LEAPs)
- Vol plays (selling premium, straddles, strangles, vol skew observations)
- Unusual options activity mentions (UOA/dark pool flow)
C. Market edge observations
- Recurring observations about sector behaviour, seasonality, or correlated moves
- Comments on market structure (market maker behaviour, options pinning, gamma)
- Screening or filtering criteria the user uses to find ideas
D. Position sizing / risk management principles
- Position size rules mentioned
- When to cut losses or take profits
- Portfolio construction thoughts
E. Watchlist / focus tickers
- Tickers mentioned repeatedly — these are likely the user's focus universe
- Context around each ticker (why they watch it, what setup they look for)
Step 6 — Analyse images
For each tweet that has media_local_paths, check if the files exist in the archive's media/ directory.
Read any image files you find — they likely contain chart screenshots. Describe what the chart shows (instrument, timeframe, key levels marked, indicators) and associate that analysis back to the tweet text.
Step 7 — Write the markdown report
Write the analysis to: {archive_dir}/analysis.md
Use this structure:
# Trading Strategy Analysis: @{handle}
*Generated: {today's date}*
*Archive: {total_tweets} tweets, last updated {last_updated}*
---
## Executive Summary
2–4 sentences summarising the trader's core style and primary focus (e.g., "Primarily a short-term options flow trader focused on large-cap tech and biotech. Favours defined-risk debit spreads around catalysts with a clear bias. Repeatedly references unusual options activity as a primary signal.")
---
## Core Strategy Framework
### Setup Types
For each distinct setup identified, create a subsection:
#### {Setup Name}
- **Signal / Entry Condition:** What triggers the trade
- **Instrument:** Stock, options type, expiration preference
- **Risk Management:** Stop levels, position size, profit targets
- **Example Tweets:** Quote 1–3 representative tweets (with dates)
---
## Options Strategies Used
List each options strategy observed with:
- Strategy name
- When/why they use it (context)
- Example quotes from tweets
---
## Market Edge & Alpha Ideas
Bullet list of specific edge observations or market structure insights the user has shared. Quote directly where possible.
---
## Recurring Tickers & Focus Universe
Table of tickers mentioned 3+ times:
| Ticker | Times Mentioned | Context / Why They Watch It |
|--------|----------------|------------------------------|
| ... | ... | ... |
---
## Risk Management Principles
Bullet list of any sizing, stop, or portfolio management rules mentioned.
---
## Key Quotes
The 5–10 most insightful or actionable tweets, quoted verbatim with dates.
---
## Image Analysis
For each analysed chart image:
- Tweet date and text summary
- Chart description (instrument, timeframe, key levels/indicators marked)
---
*Note: This analysis is for research purposes only. Nothing here constitutes financial advice.*
Step 8 — Confirm and summarise
Tell the user:
- Where the archive is saved
- Where the analysis markdown is saved (provide the full path)
- A brief 3–5 sentence summary of the most interesting strategies/alpha ideas found
- How many tweets were analysed and how many images were reviewed
Notes
- Rate limits: tweety mirrors the Twitter web client. The download script already uses a 3-second wait between pages. Do not reduce this.
- Authentication: tweety saves session state to disk under the session name
trading_explorer. If the first run fails authentication, the user will need to runsign_inmanually once (see tweety docs). - Equity/options focus: Strictly filter. Do not include crypto, macro, or personal content in the analysis even if the user tweets about it.
- Large archives: If the tweet archive has more than 500 tweets, process them in batches to avoid context overflow. Focus on the highest-engagement tweets and those with ticker symbols first.