This article is from:Jason Goldberg
Compiled | Odaily Planet Daily (@OdailyChina); Translator | Azuma (@azuma_eth)
We deployed 22 autonomous trading AI Agents on Hyperliquid via Senpi, each configured with $1000 in real capital.
They run 24/7 — scanning the market, opening positions, setting trailing stops, managing risk — all without any human intervention.
After investing $22,000 in initial capital and executing over 5000 trades, here is our summary of lessons learned.
General Conclusions
"Fewer trades" plus "higher conviction" always equals "better results." This isn't an occasional occurrence; it holds true every time.
- Odaily Note: Fox, Bison, Ghost Fox, and Grizzly, Viper, Mamba, Anaconda mentioned later are all names of Agents executing different strategies.
As shown above, Agent "Fox" and Agent "Ghost Fox" use the same scanning tool. Fox only selectively executes some of the signals, while Ghost Fox executes more signals. The result is a 56 percentage point difference in their Return on Investment (ROI).
The real advantage lies not in the scanning tool itself, but in the discipline to wait for the right signal.
- All Agents with over 400 trades suffered significant losses.
- All Agents with fewer than 120 trades were profitable.
More trades do not mean more opportunities — they mean more ineffective trades, more fees, and more exposure to noise risk.
Profits Follow a "Power Law Distribution"
Among our best-performing Agents, 3–5 trades contributed to all the profits, while the remaining trades were mostly quickly stopped out with small losses.
Taking Fox as an example again, the three best trades (ZEC, TRUMP, FARTCOIN) collectively profited over $350; the other 46 trades collectively lost over $100; the final result was a net profit of approximately $248.
This is entirely by design. Our strategy is: enter decisively with high conviction, stop out losses quickly within minutes, let profitable positions run, and lock in some peak profits using the DSL High Water trailing stop strategy. When the average win is 10 times the average loss, you can make steady profits even with a win rate of only 43%.
Those Agents that tried to maintain a high win rate through "safe" trades all lost money — because each trade aiming for tiny profits still incurs fees and market risk.
Secret Weapon: Hyperfeed
Fox and other consistently performing Agents are built on Senpi's Hyperfeed.
Hyperfeed is a real-time tracking system that shows which assets all traders on Hyperliquid are currently making money on. It is not a historical ranking or other lagging indicator, but the actual profitable trading activity happening on the entire exchange right now.
Our core scanning tool, Emerging Movers, reads Hyperfeed's market concentration data every 90 seconds. When smart money suddenly rotates to an asset: for example, a trader suddenly jumps at least 15 places on the leaderboard, or the rate of profit contribution for an asset suddenly increases, or multiple top traders simultaneously concentrate on opening the same position, the scanner can capture the signal before the move is fully priced in.
This is the structural advantage of building strategies on Hyperliquid via Senpi. You can see in real-time where top traders' profits are concentrating and act immediately. No other exchange offers this visibility, and no other platform allows autonomous Agents to execute based on this.
All our best-performing Agents use this type of data:
- Fox / Vixen: Identify sudden smart money concentration on an asset via Emerging Movers;
- Grizzly: Analyze smart money positions on BTC via Hyperfeed before opening a position;
- Bison: Uses smart money direction as a hard condition — will not trade if the direction is opposite;
While the worst-performing Agents:
- Completely ignored smart money signals, e.g., Viper, Mamba based purely on technical analysis;
- Used outdated smart money data (Scorpion v1), treating months-old positions as new signals;
So the conclusion is very clear: Agents trading based on real-time Hyperfeed data performed significantly better than all purely technical strategies.
Mean Reversion Strategies Don't Work in Perpetual Swaps
We tested three different versions of Agents based on the logic that "price deviates too far and will soon revert." Their specific performances were as follows:
- Viper: -18%
- Mamba: -33%
- Anaconda: -22%
The result was losses across the board. The problem is that the Hyperliquid perpetual swap market is far more trend-driven than mean-reverting. Buying the dip in a downtrend is the most expensive mistake in this market. These Agents kept going long at so-called "support levels," but the price continued to trend lower for days.
The fix we are testing is adding a macro market state filter that prohibits "mean reversion strategy" dip buying when BTC's 4-hour trend is down. Preliminary results look promising; this filter could avoid 14 out of Mamba's 28 losing trades.
Don't Stick to a Single Pattern
Our newest Agent (Vixen), based on Fox's trading data, uses two截然不同的 (distinctly different) entry patterns.
- Stalker Mode: Captures signals through multiple scans where smart money is quietly accumulating an asset. This allows you to enter before the crowd rushes in. Fox's biggest profits came from this mode.
- Striker Mode: Captures violent breakout moves confirmed by volume. Enters as the move explodes, but only executes if supported by real volume (filtering fake pumps).
Fox's data shows these are two completely different sources of Alpha signals. If you only use a single entry pattern, you must choose between them and miss the other opportunity.
Agents Self-Adjust — And It Always Gets Worse
An unexpected finding was: When Agents experience consecutive losses, they attempt to "self-repair." Common repair behaviors include loosening entry conditions, increasing leverage, and removing risk protection mechanisms, but the result is always accelerated losses.
Some examples: Dire Wolf, after being down -27%, enabled 5 parallel 25x leverage positions and relaxed the order speed limit; another Agent deleted the stagnation take-profit mechanism; another raised the daily loss limit from 10% to 25%.
Our solution is to write risk protection mechanisms directly into the scanner tool code, rather than relying on the Agent's own strategy configuration. If the scanner tool doesn't output a signal, the Agent cannot execute a trade — no matter how aggressive its own configuration becomes.
Next Steps
We will continue the experiment for another 24-48 hours, then shut down Agents that have no realistic chance of recovering their capital to prevent further drain of remaining funds.
Next, we will deploy new strategy versions with protection mechanisms written into the code layer:
- Wolverine v1.1: HYPE velocity DSL trailing stop (faster profit locking in high-volatility assets);
- Mamba v2.0: Mean reversion strategy + BTC trend protection;
- Scorpion v2.0: Real-time momentum event consensus (replacing outdated whale-following strategy).
We will also:
- Standardize strategy configurations for Fox, Vixen, and Mantis: These three Agents use the same scanner tool, but their configurations have drifted. Fox's current yield is over 23%; the other two will be adjusted to the same settings;
- Redeploy new Fox/Vixen pairs using Fox's full winning configuration, including XYZ ban rules, stagnation take-profit mechanism, 10% daily loss上限 (cap), all risk gating mechanisms enabled;
- Expand single-asset hunter strategies: Grizzly's three-phase lifecycle model (Hunt → Ride → Lurk → Reload) is now applied to ETH (Polar), SOL (Kodiak), and HYPE (Wolverine).
Simultaneously, we are developing brand new strategies and testing them directly in the live market. The market itself is the laboratory. Each new strategy gets $1000 in capital and fully transparent trade records.
Our experiment runs live at strategies.senpi.ai; All strategy code is open source at: github.com/Senpi-ai/senpi-skills
22 Agents, $22,000 real capital, every trade completely public. The experiment continues.
