📈 Algo Trading News 🔹 Dynamic Algorithmic Trading in Cryptocurrency A new BTC/EUR trading algorithm has been developed using Python, which focuses on leveraging multiple long and short signals. Since its initiation on August 8th, 2025, the algorithm has processed 18 trades with a win rate of 83.3% and an average profit of 1.27% per trade. However, with complexities such as the occurrence of six open positions at peak times and false signals leading to stop losses, the need for setup consolidation is noted. The developer acknowledges the interplay between the number of open positions and BTC price, recognizing the importance of robust testing to ensure system reliability in different market conditions. 🔗 /r/algotrading 🔹 Algorithmic Trading Strategy Development for Beginners For those beginning in algorithmic trading, employing signals combined with personal metric validation is proposed as an effective starting strategy. One user shared their journey, emphasizing learning market mechanics, technical analysis, and gaining proficiency in platforms like TradingView and its scripting language, Pine Script. Building strategies, analyzing backtests, and eventually automating them for live trading were key steps highlighted. Resources like Quantconnect are recommended for understanding core principles while also providing access to community support and sample strategies. This structured approach aims to facilitate a gradual transition from manual to algorithmic trading by leveraging existing quantitative frameworks. 🔗 /r/algotrading 🔹 Developments in Multivariate Time Series for Trading Exploratory research into Multivariate Time Series (MTS) models suggests potential for understanding cross-asset relationships when framed as spatio-temporal series. Models utilize order book data to detect market dynamics across assets. Challenges include navigating noise and ensuring adequate data fidelity. Initial perceptions favor MTS for machine learning enthusiasts, but the intricate infrastructure and adequate data requirements pose significant hurdles, particularly for independent traders. This classic approach invites skepticism given historical tendencies for overfitting, stressing the importance of foundational domain knowledge and empirical validation over purely experimental architectures. 🔗 /r/quantfinance, /r/quant 🔹 Political Moves Towards a Congressional Stock Trading Ban Several members of Congress, including Representative Anna Paulina Luna and AOC, are advocating for a ban on congressional stock trading, emphasizing its importance for transparency and fairness. Speaker Mike Johnson acknowledged the proposal, highlighting that many Congress members depend on trading as an income source, although he supports the idea. The debate over this ban reflects broader concerns over potential conflicts of interest and ethical considerations in financial dealings by public officials. The push for a vote on the ban suggests significant momentum and bipartisan agreement on the issue, despite opposition from some quarters who rely on the income generated from trading. 🔗 @QuiverQuant 🔹 Controversial Stock Trades by Politicians Recently, there have been notable stock trades by politicians that raise questions about transparency and potential conflicts of interest. For instance, Nancy Pelosi's purchase of Google call options earlier this year seemed unprofitable at first, but the stock has since risen by 20%. Similarly, Representative Cleo Fields made substantial investments in Google, with large gains, amidst his role on the Financial Services Subcommittee on Investigations. These trades feed into the ongoing discourse about the ethics of stock trading by lawmakers, as it may provide undue advantages given their access to non-public information. 🔗 @QuiverQuant 🔹 Algorithmic Trading Strategy Development Insights Koroush AK, a quantitative trader, shares his extensive experience in algorithmic trading, stating he has spent over 10,000 hours perfecting strategies involving breakouts, reversals, and trends. According to him, a key to successful trading isn't merely targeting a financial figure but rather cultivating the skill to generate consistent income across varying market conditions. He emphasizes the importance of developing a robust trading strategy, dismissing the common belief of predatory stop-loss hunting, and instead highlights the need for a profitable approach, reflecting the broader sentiment within the quant trading community. 🔗 @KoroushAK