i still don't understand why everyone is NOT building 24/7 trading agents with opus 5.5 + jev
this combo builds MOST POWERFUL AI trading bots
i wrote a 6-page research paper on exactly how to find profitable strategies 24/7 with opus 5.5 + jev from scratch
along with COMPLETE CODEBASE in RUST
this is the exact system I have been running for the past 3 days are so far results are INCREDIBLE:
9. Bond yields are moving stocks, too.
On the 42 days in this 2026 sample when the 10-year yield rose by more than 3 basis points, the S&P 500 fell 0.52% on average. When it fell by more than 3 basis points, stocks gained 0.53% on average.
That relationship can change, but this year’s equity investors are clearly watching the bond market.
Donchian Channels Trading Strategy – Trading Guide and Effectiveness (Backtest Commodities And S&P 500)
The Donchian Channels (or bands) are formed by two bands: an upper band based on the high of the last N bars, and one lower band based on the low of the last N bars.
Donchian Channels work well as a trend following indicator for commodities and currencies, but not for stocks.
However, we turned the Donchian Channels upside down and made the Donchian Channels work as a mean reversion indicator for stocks.
Let’s test a simple Donchian Channel strategy on the S&P 500. We backtest the following strategy:
We buy when the close breaks above the 20-day high.
We sell/exit when the close ends below the 20-day low.
This strategy has produced this equity curve (shown below) from 1960 to October 2021 (only the long side and not including reinvested dividends).
Can the strategy be improved or made different? If you have any suggestions, please comment 👇
#tradingstrategies
The more you trade, the more you learn.
The more you learn, the less you trade.
The less you trade, the more you understand the value of patience.
Eventually you realize:
The goal was never to trade more
It was to understand your edge well enough to know when NOT to trade.
This simple mean reversion idea has been around for decades, but the backtest still makes it interesting.
Larry Connors’ %B strategy looks for short-term oversold conditions inside a long-term uptrend. The basic idea is to buy when the market becomes unusually stretched to the downside and then exit after the rebound.
I backtested the setup on the S&P 500 using SPY from 1993 until today.
What stands out is how selective the strategy is. Over more than 30 years, it generated only 63 trades and spent just 4% of the time invested. Despite that low exposure, the average gain per trade was 1.3%, with 89% of trades ending as winners.
The profit factor came in around 3, while the annual return was approximately 2.5%. That might not sound spectacular on its own, but remember that the strategy was sitting in cash roughly 96% of the time.
This is a good example of what many mean reversion strategies try to achieve: wait patiently for short-term overextension, take the trade only when the setup appears, then get out quickly when the market snaps back.
As someone who builds institutional-level quant systems, this is the closest thing to a quant desk i've seen publicly shared.
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This is a very helpful tip for determining your daily bias when day trading👇
1️⃣ Break & retest PDH = Bullish ⬆️
2️⃣ Reject at the PDH = Bearish ⬇️
3️⃣ Bounce at the PDL = Bullish ⬆️
4️⃣ Break & retest PDL = Bearish ⬇️
Watch these 2 zones closely 👀
You’ll start to notice the best move of the day doesn’t happen until you see 1 of these 4 scenarios trigger ✅
$SPY $QQQ $IWM