I wish to offer my most sincere congratulations to the President-Elect of Kenya H.E. William Ruto. You truly ran an enriching, overpowering campaign & May God help you fulfill your vision for our country. #The5th# #RutoThefifth# #Ruto# #KenyanElection2022# #KenyansPoll# #Chebukati#

The US equity market's bull run shows no signs of slowing down: The S&P 500 has recorded 124 all-time highs since the current bull market began on October 12th, 2022. Over this period, the index has rallied +118%, entering the 5th year of its bull market. This year alone, the index has posted 28 all-time highs, after 39 and 57 in 2025 and 2024, respectively. By comparison, the S&P 500 recorded 70 all-time highs in 2021 and 33 in 2020. Meanwhile, more than 1,003 trading days have passed since the S&P 500 last recorded a new 52-week low, marking the 6th-longest such streak in history. For perspective, such a streak lasted 2,556 days between 1990 and 2000, ending on November 21st. The market is experiencing a historic bull run.

The 5th Global Digital Trade Expo kicked off in Hangzhou today, with more than 2,000 global exhibitors, half of which are AI-focused. Featuring a debut AI Future exhibition area and an embodied AI competition with over 100 competing teams, the five-day event highlights cutting-edge advancements across the entire AI value chain. #GDTE#

The 4 BACKTESTING TECHNIQUES behind WINNING Strategies: i've spent the last 2 years running backtests on everything from mean reversion setups to volatility arbitrage to prediction market signals some strategies survived and tbh most of them died and the difference was never the strategy itself, it was how i tested it a backtest is not proof your strategy works, it's a stress test to see how easily it breaks here are the 4 techniques i've actually run, what worked, what broke AND what i still use --------------- technique 1: standard in-sample / out-of-sample split verdict: broken by default, NEVER TRUST THIS the setup is easy, take 5 years of data and train on the first 4, test on the last 1 the problem is subtle - every time you tweak the strategy and re-run, you're peeking at the test data and after 30 iterations your "out-of-sample" is FULLY contaminated the first strategy i ever backtested was a simple pairs trade between two energy stocks that showed a Sharpe of 2.1 on the standard split, so i deployed $2,000 of my own money and lost 40% of it in 3 months going back later i realized i'd re-run that backtest 47 times during tuning, the test data was never really untouched use this only for a quick first look, NEVER as the final validation --------------- technique 2: walk-forward validation verdict: the real workhorse, this is what i actually use instead of splitting once, you slide a window through the data train on 2018-2020, test on 2021 train on 2019-2021, test on 2022 keep sliding each test window is data the model has never seen and you get 5 or 6 test periods instead of JUST ONE what this catches: > strategies that only worked in one regime (the pattern shows up immediately) > parameters that shift wildly when retuned (unstable strategy, red flag) > strategies that survive across every window (this is real edge) at our fund we killed a stat arb strategy that showed Sharpe 2.4 on a standard split, but walk-forward revealed it worked beautifully in 2019-2020 and completely died in 2021-2022, the regime had shifted underneath us and it saved us months of losses but this is slower and more painful than a standard split and it's also the reason institutional backtests match live P&L :) --------------- technique 3: purged k-fold cross-validation verdict: fixes a hidden bug in walk-forward financial data has memory, today's price is not independent of yesterday's when your training window ends on december 31 and your test window starts january 1, information leaks across that boundary and your Sharpe looks better than it should purged k-fold fixes this, Marcos Lopez de Prado covers it in Advances in Financial Machine Learning the idea is simple: > split data into folds like standard cross-validation > when a fold is used for testing, remove the adjacent observations that overlap in time > this eliminates the leakage a QUANT friend of mine who runs an ML-based factor model showed me his numbers before and after adding purging, Sharpe dropped from 1.9 to 1.4 on the same strategy with the same data and the extra 0.5 was pure leakage he didn't know he had use this when you're training ML models on financial data, the leakage in tree-based models is brutal without it --------------- technique 4: monte carlo trade shuffling verdict: the reality check that saves capital EVERY SINGLE TIME your backtest shows one sequence of trades, Monte Carlo randomizes the order and runs it thousands of times why this matters: > your backtest might have gotten lucky with sequencing, what if the drawdown happened in month 2 instead of month 10 > the max drawdown you observed is one path, Monte Carlo shows the full range > the 5th percentile drawdown is often 2 to 3 times worse than what you saw few months ago (during the hype of 15-min BTC markets) i built a systematic prediction market strategy that showed 12% max drawdown across 18 months of backtest, but before deploying i ran Monte Carlo with 10,000 shuffled sequences and the 5th percentile scenario showed a 34% drawdown ofc i didn't deploy at full size, i sized it at 25% of what i originally planned and three months in the strategy hit a 22% drawdown, but the smaller size meant i could hold through it and the strategy recovered to finish the year up 31% Monte Carlo is why i stayed in that trade instead of blowing up --------------- what i actually use in production NOW: > walk-forward validation as the primary test > purged k-fold when the strategy uses ML models > Monte Carlo shuffling on strategies that survive both, before any real capital > standard in-sample/out-of-sample only for the very first pass (rarely tho) if your backtest is designed to make you feel good, it's designed to LOSE you money for real.

The AI boom is transforming Japan's economy. Japan's exports surged +23% YoY in July, the largest annual increase since October 2022. This is up from June's +19% gain and marks the 5th consecutive monthly increase in growth. The surge was driven by electronic component and semiconductor exports, which soared +49% YoY, fueled by demand from AI and data center investment. At the same time, passenger car exports rose +21% YoY. Shipments to the US rose +22% YoY, while exports to China and Europe jumped +26% and +19%, respectively. The improvement also comes as the Yen weakened +11% YoY last month, to a 40-year low against the US Dollar, averaging 161.83, making Japanese exports more competitive globally. The global AI investment wave is driving a sharp increase in Japanese exports.

The question I get most right now is "what should I turn into an AI agent?" Here is the 5 part test I use (works for Claude/Codex/Grokbot etc): 1. It has a repeated trigger, so the same kind of task keeps happening over and over. 2. The inputs are stable, so the information comes in a predictable shape every time. 3. The tools are clear, so there is a defined set of things it can actually go do. 4. There is a measurable finish line, so you can tell when it is done and whether it worked. 5. There is judgment in the middle, so each run is a little different and needs a real decision made in the moment. The first 4 are really just asking "can this be automated at all?" The 5th is the one that matters, because judgment in the middle is what makes it an agent instead of a simple automation. Once you see it this way, you start spotting agent work pretty much everywhere.