📈 Algo Trading News 🔹 Profitability Challenges in FX Trading A Reddit user expressed challenges in achieving profitability while backtesting a trading strategy using the triple barrier method on FX markets. After accounting for the bid-ask spread and liquidation, the user found an average profit rate of only 35% per trade. This highlights the inherent difficulties retail traders face when dealing with spreads skewed by platforms, and the intense competition from larger financial entities that quickly absorb any emerging arbitrage opportunities. The post underscores the importance of understanding realistic market conditions in strategy testing and selection, and the challenges of overcoming transaction costs and slippage for retail traders. 🔗 /r/algotrading 🔹 Historical Option Chain Data Challenges in Cryptocurrency A nascent trader is in search of historical option chain data for cryptocurrencies to backtest strategies involving implied volatility. With standard data services costing upwards of $1200 per month, the trader explored alternatives like Polygon and QuantConnect. Issues like incomplete API functionalities of free services highlight the challenges traders face in accessing affordable, comprehensive datasets essential for backtesting and strategy development in the cryptocurrency markets. There is also a suggestion to build a personal database by scraping data to avoid reliance on expensive or suboptimal data services. 🔗 /r/algotrading 🔹 Coding Assistance in Developing Trading Bots In developing trading bots, traders often face challenges like inaccurate stop losses, size calculations, and trailing stops. For those using AI assistance for coding, such as with Claude in NinjaTrader, the process often involves correcting suboptimal code outputs. Community responses suggest improving coding skills to complement AI tool usage, using AI primarily for boilerplate code and investigating platform-specific communities like NinjaTrader forums for direct guidance. This collaborative approach between human review and AI assistance highlights the potential of leveraging technological innovation in algorithmic trading development, albeit with a need for rigorous human oversight and skill enhancement. 🔗 /r/algotrading 🔹 Representative Proposes Congressional Stock Trading Ban Representative Anna Paulina Luna has announced plans to file a discharge petition to initiate a congressional vote for a stock trading ban, according to a post by @QuiverQuant. This rarely used legislative mechanism indicates a critical development in the regulation of personal trading activities among legislators, potentially increasing transparency and addressing conflicts of interest. The announcement suggests that the proposal may face resistance, highlighting possible intra-party tensions regarding the issue. 🔗 @QuiverQuant 🔹 Pairs Trading Strategy in Algorithmic Trading @macro_synergy shared a code example of a Pairs Trading Strategy using Python, which capitalizes on the tendency of certain assets to move synchronously, allowing traders to exploit the mean reversion potential of price spreads. By identifying pairs of assets with high correlation, traders aim to profit when these price differences deviate from their historical norm, anticipating that they will revert over time. This strategy emphasizes careful statistical analysis and model implementation as core to its success in automated trading environments. 🔗 @macro_synergy 🔹 Synthetic Data and LLM Training Enhancements @karpathy discussed the transformation of human-readable content into formats suitable for Large Language Models (LLM), focusing on creating enriched datasets for training. By converting traditional learning materials into structured, LLM-readable formats, such as markdown with extracted problem examples and synthetic variations, researchers can enhance LLMs’ ability to process and learn complex information. This approach aims to move beyond simple text prediction models, creating more effective educational and application engines through diversified training data. 🔗 @karpathy