From Valet to $20 Million: How Jack Kellogg Built a Trading Record

Jack Kellogg saved $10,000 working as a valet and entered a trading challenge in 2017 instead of attending college. Early on he suffered a steep drawdown — losing about one-third of his account in two days — but by September of that year he had found consistency and continued to scale.

Kellogg's reported year-by-year milestones in the supplied account include:

• 2020: Passed the $1 million mark and closed the year with $1.9 million in career profits.
• 2021: A particularly large performance year that included a $624K single day, a $1.3M week, and pushed career profits to $8.6 million.
• 2022: Added roughly $1.7 million while the broader market was down and crossed $20 million in cumulative trading profits (accounting for losses).

Kellogg and the author used a specific stock-selection framework that emphasizes concrete criteria:

• Low float: Smaller available share counts help amplify price moves when demand rises. CDIO was cited as having a 16 million share float, which the author called "decently close" to their 10 million target.

• News catalyst: CDIO had a delisting-risk notification tied to a sub-$1 price, which created a spike as market participants reacted.

The CDIO example shows the sequence: find a candidate that fits the float, sector, and news profile, then apply proven intraday patterns to trade the move.

The trading approach relies on a small set of repeatable patterns that tend to occur in volatile situations. The supplied account argues that people behave predictably under stress, which produces similar intraday formations. The core procedural points are:

• Find the right stocks first using filters for float, sector, and news.
• Apply predefined volatile-stock entry and exit patterns in real time.
• Use scans and alerts (the author mentions AI-enhanced scans and alerts) to surface candidates faster than manual searching.

An alert system was cited as part of the workflow that flagged CDIO. The article notes that using automated scans and alerts helps traders spot breakouts and trend reversals early, follow big-money flow, and simplify the watchlist process. The text specifically references an AI trading bot used by the author to identify stocks and issue alerts.

• Start small and accept steep learning curves: Kellogg lost a large portion of his early capital but persisted and found a replicable approach.
• Use objective filters: Float, sector momentum, and news catalysts were core filters that led to tradeable setups.
• Learn specific intraday patterns: Repetition and experience in real time are presented as the route to recognizing profitable setups.
• Use tools to scale: Alerts and scans were important for finding opportunities quickly.

Kellogg's story is presented as an example of scaling a small starting stake into large cumulative profits by combining concrete stock selection filters, repeatable intraday patterns, and systematic use of alerts and scans.