https://www.tradingview.com/script/OUgjD1sH-Quant-Rick-Trend-Diagnostics/ What it does and how to read it ================================================================ This indicator gives one reading of where 9 asset classes stand on trend: large caps, mid caps, small caps, equal weight S&P, bonds, gold, commodities, the US dollar and crypto. It is built on Rob Carver's trend rules from "Advanced Futures Trading Strategies". It doesn't give buy or sell signals. It tells you what state each trend is in. WHAT YOU SEE ON THE CHART ---------------------------------------------------------------- Two EMAs sit on price, a fast one and a slow one. By default they are 32 and 128 days. The space between them is shaded green when the fast EMA is above the slow one and red when it is below. This is the simplest form of the trend signal. WHAT YOU SEE IN THE TABLE ---------------------------------------------------------------- There is one row per asset and these columns: EWMAC (trend strength) The fast EMA minus the slow EMA, divided by the asset's daily volatility. Dividing by volatility lets you compare a calm bond ETF with a volatile asset like Bitcoin on the same scale. Four speeds are averaged: 8/32, 16/64, 32/128 and 64/256 days. Breakout (position in the range) Where price sits inside its recent high-low range. Near the top of the range it is positive, near the bottom it is negative. It averages four lookbacks (20, 40, 80 and 160 days) and is smoothed so it doesn't jump around. Accel (acceleration) How much the trend forecast changed over the last 16, 32 and 64 days. A positive number means the trend is getting stronger. A negative number means it is weakening. This is the column that changes first when a trend starts to roll over. Combined A weighted blend of the three rules: 40% EWMAC, 40% Breakout and 20% Accel. Agree How many of the three rules point the same way as Combined. 3/3 means

For sourcecode: rickquant.com for script for free use on tradingview https://www.tradingview.com/script/x53lP0Fc-Growth-Inflation-Quadrant-2/ Growth / Inflation Quadrant – what this indicator does This is a TradingView Pine Script (v6) indicator. It sorts the market into one of four macro regimes, using two market-based proxies in place of economic surprise data. 1. The two proxies Growth = the 63-bar % change in the copper/gold ratio. Copper is a cyclical industrial metal and gold is a safe haven, so a rising ratio means the market is pricing in stronger growth. Inflation = the 63-bar change in 10y breakeven inflation (FRED T10YIE), converted to basis points. FRED quotes it in percent, so multiplying by 100 gives bp. 63 trading days is about 3 months. 2. The four regimes The signs of the two changes pick the regime: - Growth up, inflation down = 1 GOLDILOCKS (green). Historically best for equities. - Growth up, inflation up = 2 REFLATION (orange). Commodities, value, cyclicals. - Growth down, inflation up = 3 STAGFLATION (red). Worst for both stocks and bonds. - Growth down, inflation down = 4 DEFLATION / SLOWDOWN (blue). Government bonds do best. 3. Persistence filter The regime only updates once the raw signal has been the same for N bars in a row (5 by default). Until then it keeps the old regime. This cuts down on flip-flopping when one of the proxies sits near zero. It starts in regime 4 (blue) until the first stable reading. 4. What you see on the chart - Lime columns show the growth proxy in %. - A fuchsia line shows the inflation proxy, divided by 10 so it fits on the same scale. A +50bp move plots as 5. - The background is tinted in the regime colour. - A table in the top-right corner shows the current values, the regime name, and what historically did best in that regime. - An alert fires on the bar where the regime changes. 5. Things worth knowing

CODE IS IN TRANSCRIPT ON BOTTOM. Quick context on the new indicator, because the theory matters more than the code. The code is about ten lines. The idea is time-series momentum, also called trend following. Not cross-sectional momentum, where you rank assets against each other like we do in the rotation models. Here the only question is: is this asset going up or down compared to its own past? If it’s up, you’re long. If it’s down, you step aside or go short. Why would that work at all? The way I see it there are two sides. The behavioral side is that people underreact to news at first. Information gets priced in slowly, then the crowd piles in late and overshoots. People react to people reacting to people, right? That’s what makes a trend. The risk side is that trend gets paid because it’s uncomfortable to hold. It whipsaws in choppy markets and bleeds for months, and most people quit right before it pays off. The evidence is about as strong as it gets in finance. Moskowitz, Ooi and Pedersen showed it across 58 futures markets. Hurst, Ooi and Pedersen later took it back more than a century with “A Century of Evidence on Trend-Following Investing”. In Ilmanen’s book (Investing Amid Low Expected Returns), trend made money in basically every asset class they studied, with Sharpe ratios from 0.44 in currencies to 0.62 in bonds. So why 1, 3 AND 12 months instead of just one? Layers, not heroes. The 12-month window is the slow, reliable signal, but it’s late at turning points. The 1-month window reacts fast but is noisy. The 3-month sits in between. Averaging the three gives you a signal that’s never fully wrong-footed. If all three agree you get +1 or -1, a clean trend. If they disagree you get +0.33 or -0.33, which tells you the trend is weakening or turning. This blend is basically what AQR runs. There’s one more setting, “skip most recent bars”. Set it to 21 and you get Ilmanen’s own conservative version, which ignores the last month. Think of it as a cheap way to account for trading costs, and it also stops you reacting to last week’s noise.

Who he is and what he did Edward Thorp was born in August 1932, in the middle of the Depression. He grew up in a working-class family with parents on defense-industry shifts, which left him and his brother mostly on their own. He was an early reader who found most of his interests were shared only by adults, and he started earning money young: Kool-Aid for a penny a glass, then a paper route. He did a physics PhD, switched into math, and ended up an instructor at MIT, then New Mexico State, then UC Irvine. Along the way he worked out how to beat blackjack, built a wearable computer to beat roulette with Claude Shannon, and then moved into markets. There he priced warrants and options, ran what he describes as the first quantitative market-neutral hedge fund, and compounded at roughly 19 to 21 percent a year for decades. Both hosts open by stressing how extreme the record is: 227 profitable months out of 230. Blackjack and gambling In 1959 he read a statistics paper on a near-break-even blackjack strategy and took a $10 gamble on a Vegas trip. At the table he noticed that neither the players nor the casino understood the game. The key insight was that the deck isn't reshuffled after every hand, so as cards leave, the odds shift. He had a computer remove four aces and saw the casino's edge jump by almost three percent, which told him that a deck rich in aces and tens favors the player. He then needed an IBM 704 at MIT because the calculation was far too large to do by hand. The rest was betting: big when the deck favors you, small when it doesn't. The AMS abstract committee assumed he was a crank, since theorems said casino games couldn't be beaten. A member who knew him vouched for him. Manny Kimmel then bankrolled a $10,000 trip, and Beat the Dealer became a bestseller. He says gambling was the best training ground for investing, because it teaches you to compute odds, manage money, and act when you have an advantage. Mathematics and probability He shows an instinct for turning a vague belief into a calculation. The roulette idea came from physics: the ball orbits like a planet, so if you can measure the position and velocity of the rotor and ball, you can predict a useful region of the wheel. The warrant idea came from realizing that most of the uncertainty in a warrant's price comes from the underlying stock and the time left. In the transcript he says he derived the option formula himself by reasoning about the hedge, and that it matched Black and Scholes when their paper came out in 1973. He is also very comfortable with the limits of probability: in markets you often know the payoffs but not the probabilities, so you estimate a distribution and think through how bad things could be compared to your most likely case. If even the bad case looks good, he says, you have something worth playing.

Bernstein was a neurologist who got obsessed with a T. Rowe Price study showing a four-asset portfolio beating 75% of professional money managers. So he built his own spreadsheets, ran the numbers back to 1926. What he found is the reason "robustness beats performance" isn't just something I say, it's something the data has been screaming since before most of us were trading. The core move of the book: take five different historical windows, calculate the mathematically perfect allocation for each one with full hindsight, then watch every single one of those "optimal" portfolios get destroyed the moment you carry it forward. 100% Japan in 1989. 99.8% precious metals in 1970. Every one a genius pick looking backward, every one a disaster looking forward. That's what happens when a fancy-looking tool gets fed recency bias.a