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  1. Sometimes I feel like the only #OpenSource #Linux-using #DayTrader in these parts. But that’s okay—I’ll keep posting my daily condensed #PreMarket report [for free].

    Why? Because some geniuses at E-Trade let me limit trade before the bell rings. 🚀

    #DayTrading #Linux #OpenSource #PreMarket #ETrade #TradingStrategies #FinancialFreedom #TechTrader #Tech

  2. Bitcoin ETFs Gain Fresh Case After $89M Coldcard Drain

    Bloomberg Intelligence senior ETF analyst Eric Balchunas said on Aug. 2 that the Coldcard security failure strengthens the case for U.S. spot Bitcoin ETFs, especially for investors who want long-term price exposure without managing private keys. His comments followed Galaxy Research’s estimate that three suspected attack waves drained 1,367.05 BTC, worth about $88.6 million, from 4,585 addresses........Continue reading.... By: Olivia Stephanie Source: Crypto News . Critics: ETFs are […]

    onlinemarketingscoops.com/2026

  3. The Signal In The Noise: How To Filter Thousands of Stocks Down To a Few Winners

    Stock investing is one of the best ways to grow your money and earn financial freedom. But that doesn’t mean it’s easy to invest in stocks. You want the highest capital appreciation possible, but unfortunately, risk and reward are intertwined. One of the most reliable ways to make money when you invest in stocks is to conduct ample investment research before purchasing and then to hold the most promising stocks long-term......Continue reading.... By: Riley Adams Source: […]

    onlinemarketingscoops.com/2026

  4. Coinbase is launching AI tools that give investment advice and make trades on your behalf

    Coinbase Global (COIN) is rolling out two new AI investing tools as financial apps and investing apps race…
    #NewsBeep #News #Artificialintelligence #AI #ArtificialIntelligence #Branzburg #CA #Canada #coinbase #everydaycustomers #financialadvice #financialapps #investingtools #MaxBranzburg #retailcustomers #Technology #tradingstrategies
    newsbeep.com/ca/742900/

  5. Originally from Hristo H. here: linkedin.com/posts/hristo-sv-h

    The 9 Deadly Sins of Backtesting (and How to Fix Them) - Part I

    Many trading strategies do not fail because of unforeseen market events; they fail at their inception—specifically, the moment the researcher ceases to maintain absolute rigor in the backtesting environment. The fundamental premise of successful quantitative research is not the discovery of clever signals, but the systematic and ruthless elimination of bad ideas through the identification of false positives. On paper, you see a clean equity curve; in production, the profits disappear.

    1. Survivorship Bias:
    This error occurs when a researcher focuses exclusively on assets that have survived a selection process while overlooking those that did not, leading to a sample that is fundamentally unrepresentative of the historical population. For example, a momentum strategy applied to the S&P 100 can yield a 26% CAGR, but once failed and delisted companies are reintegrated, the edge plummet to 12.2%. In the Nasdaq 100, the discrepancy is even more extreme, with drawdowns exploding from 41% to 83% when "forgotten" losses are included. Fix: Point-in-time (PIT) data. This means using a database that reproduces the exact information available at the historical moment, ensuring you only trade assets that were actually listed and tradable on that specific day.

    2. Forward-Looking Bias:
    Often called "trading with tomorrow's newspaper," this involves incorporating information into a signal that would not have been available at the moment of execution. A primary culprit is backward price adjustment: if a stock trades at $100 on Jan 1 and splits 2:1 on Feb 1, a backward-adjusted dataset will rewrite the Jan 1 price to $50. Any signal using a price filter on Jan 1 effectively "knows" the split is coming. Fix: Use raw prices for signals and forward price adjustment (anchoring to the first available date) for performance measurement to maintain temporal integrity.

    3. Transaction Costs:
    Many strategies show a small edge that evaporates once realistic costs are applied. Standard OHLCV data often suffers from "outlying" values due to small orders at regional exchanges and cannot capture the reality of market depth or slippage. Fix: Use Transaction Cost Analysis (TCA) benchmarks like Arrival Price—the mid-quote price at the moment the order is submitted—and model non-linear market impact that grows with your order size.

    4. Statistical Significance:
    In finance, we routinely accept discoveries where the t-statistic exceeds 2.0, yet a 10-trade "hot streak" is often just a stochastic realization of noise. Relying on small sample sizes provides a distorted picture of a strategy's true predictive power. Fix: Look for hundreds or thousands of independent "bets" across the timeline. Use a t-stat > 2.0 as a bare minimum requirement to ensure results are unlikely to have occurred by chance.

    To be continued ...
    #QuantitativeFinance #TradingStrategies #Backtesting #DataScience #FinTech #AlgorithmicTrading

  6. Calcular Martingale na Olimp Trade: Guia Completo#GuiadeNegociação #CalculatingMartingale #investmentstrategies #OnlineTrading #Tradingstrategies Aprenda como calcular martingale na Olimp Trade com nosso guia completo. Domine essa estratégia de gerenciamento de risco e otimize suas operações na plataforma.
    acryptoaday.com/calcular-marti

  7. Copy Trade: Entenda o Que É e Como Funciona#Corretoras #CopyTradeExplained #InvestimentoAutomático #SocialTrading #Tradingstrategies Descubra o que é copy trade, um método para replicar estratégias de investimento de traders experientes e otimizar seus resultados financeiros.
    cleverpay.io/copy-trade-entend

  8. 🕵️ 2/7: The Illusion of the Obvious
    Just because a pattern is clear doesn't mean it's actionable. The best opportunities in trading are often hidden; they require a deeper analysis and a knack for seeing beyond the general consensus. #TradingStrategies #StockMarket

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