Algotrading on Robinhood Agentic for a week

AI-резюме

=== SUMMARY === - The author is testing 13 independent autonomous trading agents across Robinhood, Coinbase, and Kalshi, achieving a 6.97% return in the first week. - Strategies include LLM sentiment inversion on Trump posts, tracking ClinicalTrials.gov Phase 3 entries, mirroring Congressional STOCK Act disclosures, and crypto momentum. - Quality assessment: Speculation/Experimental. The author explicitly notes it is "way too early to read anything into it" and highlights unresolved issues with portfolio concentration and threshold calibration. === SENTIMENT === MIXED === TRADE IDEAS === TLT - LONG | confidence: 0.70 | sentiment: +0.50 Speaker: u/randbobaccount Thesis: 1. THE FACT: An LLM scores Trump's Truth Social posts for directional tone with high confidence (>0.8). 2. THE BRIDGE: The author's strategy assumes that highly confident bullish posts from this feed are contrarian indicators for risk assets. 3. THE VERDICT: Buy safe-haven assets like TLT (or GLD) when the LLM detects high-confidence bullish sentiment. 4. RISKS: The signal may be noise, or the LLM confidence threshold (0.8) is arbitrarily set and uncalibrated. Timeframe: short-term Key Points: - LLM scores Trump posts for sentiment. - Acts only on >0.8 confidence. - Inverts bullish sentiment to buy bonds/gold. - Threshold is currently uncalibrated. SPY - LONG | confidence: 0.70 | sentiment: +0.50 Speaker: u/randbobaccount Thesis: 1. THE FACT: An LLM scores Trump's Truth Social posts for directional tone with high confidence (>0.8). 2. THE BRIDGE: The strategy assumes that highly confident bearish posts are contrarian indicators, signaling a buying opportunity for equities. 3. THE VERDICT: Buy broad market equities (SPY or QQQ) when the LLM detects high-confidence bearish sentiment. 4. RISKS: The market may actually sell off on bearish political news, breaking the inversion logic. Timeframe: short-term Key Points: - LLM scores Trump posts for sentiment. - Acts only on >0.8 confidence. - Inverts bear

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Been running 13 autonomous agents on a live account across Robinhood, coinbase and Kalshi, each with its own capital slice and no coordination layer between them. Up 6.97% this week overall.

Some of the designs:

Truth Social, inverted. An LLM scores every new Trump post for directional tone with a 0 to 1 confidence. Acts only above 0.8, then inverts the call. High confidence bullish rotates to TLT/GLD, high confidence bearish to SPY/QQQ. Below threshold, nothing happens.

Same feed, patient. A second agent waits five days after a post before acting, on the theory that the first two sessions are reaction and whatever is left after that is the real move. Longer holds. Running both is how I am trying to find out whether the signal is in the reaction or in what survives it.

ClinicalTrials.gov. Buys a whitelisted biotech sponsor when a trial registers a Phase 3 entry, holds 60 days. Favourite idea in here, because the source is a government registry nobody is racing to price.

STOCK Act disclosures. Mirrors purchase filings from a set of high-volume House and Senate filers. Next bar, 60 day hold, capped at 8 concurrent.

Crypto, winner take all. Ten liquid Coinbase pairs ranked every day on 7 day momentum, volume trend and volatility. It holds the single highest-conviction coin and nothing else, re-picked daily. Zero diversification by design, but it’s been performing well.

Kalshi versus the forecasters. Compares Kalshi economic-data prices against figures institutions have already published (Cleveland Fed nowcast, wire consensus, CME FedWatch) and acts only when the two disagree by a real margin. It does not forecast anything itself. Not seeing good results on Kalshi. 

Plus a sector contrarian that buys the two weakest of the 11 S&amp;P sectors, a WallStreetBets agent weighted by sentiment times upvotes, and two crypto rotations.

First week is in the screenshots. Way too early to read anything into it.

Still working out: 

One, overlap. 13 agents running independently with nothing netting them, and several keep landing on the same names from completely different logic. I think I am diversified across 13 strategies and I genuinely do not know how concentrated I actually am. Is there a good way to measure that across agents without just collapsing everything into one portfolio?

Two, the confidence threshold. The Trump agent only acts above 0.8. I picked 0.8 by feel. Does anyone actually calibrate this, as in check whether the model is right 80% of the time when it says 0.8, and set the cutoff from that? Or is everyone eyeballing it.

What would you add?

Идеи

u/randbobaccount Reddit r/algotrading

An LLM scores Trump's Truth Social posts for directional tone with high confidence (>0.8). The author's strategy assumes that highly confident bullish posts from this feed are contrarian indicators for risk assets. Buy safe-haven assets like TLT (or GLD) when the LLM detects high-confidence bullish sentiment. The signal may be noise, or the LLM confidence threshold (0.8) is arbitrarily set and uncalibrated.

u/randbobaccount Reddit r/algotrading

Biotech sponsors register Phase 3 trial entries on the government registry ClinicalTrials.gov. This data source is public but not heavily raced by high-frequency algorithms, leaving a window to price in the catalyst. Buy whitelisted biotech stocks (or proxy XBI) upon Phase 3 trial registration and hold for 60 days. Phase 3 trials can still fail, and the 60-day hold period exposes the trade to broader sector volatility.

u/randbobaccount Reddit r/algotrading

An LLM scores Trump's Truth Social posts for directional tone with high confidence (>0.8). The strategy assumes that highly confident bearish posts are contrarian indicators, signaling a buying opportunity for equities. Buy broad market equities (SPY or QQQ) when the LLM detects high-confidence bearish sentiment. The market may actually sell off on bearish political news, breaking the inversion logic.

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Идеи

Ещё от Reddit — r/algotrading