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The speaker discussed their experience with algorithmic trading, highlighting strategies such as Momentum, Mean Reversion, and Arbitrage. They emphasized the importance of speed in executing trades, especially in Arbitrage, and noted common practices like Insider Trading and Manipulation in low-liquidity markets. Managing money in low liquidity markets, carrying out carry trades, and trading trends based on candlestick analysis were also discussed. The speaker shared their experience with trading algorithms on the Bitget exchange, renting out algorithms to influencers and facing challenges with public reactions and competitive advantage. They also touched on the challenges of trading on the stock market, questioning the effectiveness of professional trading firms and emphasizing the importance of understanding the market and risks involved in trading. The speaker highlighted the importance of risk management, continuous improvement, and the challenges of building predictive models for trading. They discussed their journey in trading, trying various strategies and algorithms with limited success, ultimately questioning the value of trading. The speaker also discussed sentiment analysis, the challenges of predicting price changes, and the complexity of trading compared to building a business. They emphasized the importance of understanding when a strategy works and when it fails in trading. The conversation delved into trading strategies, adapting to market phases, and the complexities of developing successful strategies, particularly in arbitrage opportunities. The importance of data analysis, model refinement, and making quick decisions in the market were highlighted. The speaker discussed the challenges of identifying trends, interpreting market signals, and building models for informed trading decisions. They shared their experience developing a trading strategy based on mean reversion and advised against trading, recommending exploring other avenues for creating value. The speaker also discussed using Ichimoku indicators, analyzing price movements, and the importance of additional information beyond technical indicators in trading decisions. The challenges faced by quantitative traders, portfolio strategies, and the use of semantic layers for data analysis in trading were also touched upon. The conversation discussed using a semantic layer for training models, high-frequency trading strategies, and the importance of considering commissions. The speaker recommended classic approaches for model training and blending techniques based on effectiveness. They emphasized the lack of public information on trading strategies and the importance of experimentation. The conversation also discussed regression in data science, reference metrics for model evaluation, and the use of libraries like Facebook's Prophet for analysis. ClickHouse for data storage and analysis, managing disk space, script failures, and database performance were also mentioned. The speaker shared their experience with ClickHouse for real-time data processing and storing historical data on S3. The conversation concluded with the speaker encouraging further discussions.
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