Instead of continuing to torture the data—and ourselves—in effort to find meaning in the noise, we should instead focus on building portfolios that are robust to many potential outcomes rather than being perfect for a single one. It is an approach where we openly admit, “I don’t know.” This admission of uncertainty allows us to focus on being vaguely right instead of risking being precisely wrong.and answer Continuing data focus ourselves outcome perfect takes torture
The film takes place in Berlin, 1989, on the eve of the collapse of the Berlin Wall and the shifting of superpower alliances.
Atomic Blonde is an upcoming American spy action thriller film directed by David Leitch and written by Kurt Johnstad.agent alliance berlin david film john Leitch spy takes West
This post will be about replicating the Marcos Lopez de Prado algorithm from his paper building diversified portfolios that outperform out of sample. This algorithm is one that attempts to make a tradeoff between the classic mean-variance optimization algorithm that takes into account a covariance structure, but is unstable, and an inverse volatility algorithm that ignores covariance, but is more stable.algorithm attempts classic covariance etf mean optimization takes tradeoff variance
Technology has finally advanced to the point where marketers can use real-time data in a way that is both meaningful to customers and profitable for companies. But here’s what this example looks like when we activate Jane’s data: Three days after her online purchase, the retailer sends Jane a health-themed email.advantage data homeowner Jane mom pants retailer store takes yoga
OppenheimerFunds is known for its revenue-weighted ETFs, which today command nearly $2 billion in combined assets. The lineup of nine ETFs includes the likes of the Oppenheimer Large Cap Revenue ETF (RWL) and the Oppenheimer Small Cap Revenue ETF (RWJ), each with more than $520 million in assets.cap etf factor Oppenheimer returns revenue stock takes weighted weighting
Is it the next gold rush or just a hype ?
This got us thinking if such a smart investor with a respectable track record and somebody who is famous for seeing the big picture well ahead of others is telling us that water is the next big thing it might be worth a deeper look into it.almond Big Farmer gallon invest investors surface takes water years
In Python for Finance, the author gives an example of three ways in chapter 1 to do a large mathematical calculation using different Python libraries.
We are experimenting the hell out of this pure lambda or functional programming style right now, so rewrite the following examples with lambdas only.expression implementation inter Lambda note seconds stays string takes variable
June was a good month for the Trend Following Wizards, with all funds but one returning positive numbers, with a handful in double-digit territory, including a 27% return for the highest performance.
He is considered as a “second-generation” Turtle.Program tracked: Diversified Program.acquires AHL division gradually Man Subsequently systematic takes trading YTD
There are two types of traders: The quick and the dead. We all stood around in direct competition with each other, doing math in our heads as quickly as we could, trying to recognize trading opportunities and executing them with our hand signals and our voices.competition execute execution math matter quickly recognize screen solid takes
Suppose that you had the magical ability to foresee turns in the business cycle before they happened.
The following chart shows the hypothetical historical performance of an investment strategy that times the market on perfect knowledge of future recession dates.1937 recession 1974 recession 20 months 7 years >importantly >recall >ultimately access add advance aggregate indices amplitude balance beginning bids bring build built business cycle buys captured captured downturn captured downturns capturing charts checking checks closely closing collect common completion contact continues continuous continuous checking crossover crucially cumulative cycle cyclicality deep degrees deterioration deviations diluted dotted red downside downturn downturns earlier effects examine examined explain falling falls fluctuating frequently gain gains gap gap losses general generating greater portion greater presence growth signals happened holds implementing improve increased increasing incur incurred index indices insights interacts introduce introduced key late lead left leg longer moving losses relative market market takers maximum drawdown met midpoints mma movements moving moving averages negative trend negativity occur occurred occurs offset optimize orange oscillation oscillations outperforms participate passes peaks perfectly continuous places produced protect published pulled random reach recession recessions recoveries reducing refer remains represents responds rises sells shorten shown significant sine wave situations slowly smaller specifies stay stop strategy strategy checks strategy ends strategy outperforms strategy produces strategy relative strategy sells strategy underperforms stuff subsequent successfully suffer suggest takes tested timing trader trail trailing trailing annual trailing stop trails transaction traversing trend timing triggering trip transaction turns upside whipsaw whipsaws “continuous” “knob” “volatility”