現今隨著網際網路技術及資訊科技發展日益蓬勃，演算法交易——將交易策略邏輯預先撰寫成電腦程式以進行線上自動交易也隨之興盛，尤其在漲跌劇烈的交易市場中，使用能因應市場局勢進行自動買賣的量化交易程式更是在金融市場中引起各大金融機構及政府的關注。如何利用程式在瞬息萬變的金融市場中自動且具自我調適能力的進行交易已成為近年全體金融市場競相發展及追求的熱門研究議題。

本研究提出一套全新的線上自適應交易演算法可應用於貨幣市場、加密貨幣市場、期貨市場等金融交易市場中。於演算法前段將以簡化群體演算法進行本研究新提出的彈性網格參數優化，後段將資料匯入人工神經網路模型進行訓練，以助於在未來的各種情況下能自動選擇出相對應適合的參數組合，進行彈性網格的建構與交易。

本套全新的交易演算法得以於劇烈變動的金融市場中，適應市場多變的情境，自動適時的做出交易策略的調整並且掛單買賣，在獲利的同時，兼具風險控制考量，屬於一套兼具穩健性及獲利性之平衡型交易演算法。

In our modern society, with the development of Internet and information system, pre-programing algorithmic trading strategies for online automatic trading has also flourished, especially in the rapidly fluctuating trading market. Using quantitative trading programs that can automatically trade in response to market conditions has attracted the attention of major financial institutions and governments in the financial market. How to use self-adaptive programs to automatically trade in the ever-changing financial market has become a popular research topic for the development and pursuit of all financial markets in recent years.
This research proposes an online self-adaptive trading algorithm that can be applied to financial markets such as stock market, currency markets, cryptocurrency markets, futures markets, etc. In the first part of the algorithm, the simplified swarm optimization will be used to optimize the parameters of the newly proposed flexible grid in this research. Then the data will be imported into the artificial neural network model for training in the latter part, helping the trading model automatically select the appropriate parameters for construction flexible grid corresponding to the market conditions.
The greatest contribution of the research is to provide a whole new trading algorithm that can adapt to the dynamic trading market, automatically make suitable adjustments to current trading strategy and place real-time orders. The algorithm controlling both profit and risk, which can seem as a balanced trading algorithm that are robust and profitable.

1. IOSCO, J., Regulatory Issues Raised by the Impact of Technological Changes on Market Integrity and Efficiency. Consultation Report, 2011.
2. Allen, F., J. McAndrews, and P. Strahan, E-finance: an introduction. Journal of financial services research, 2002. 22(1): p. 5-27.
3. Shahrokhi, M., E‐finance: status, innovations, resources and future challenges. Managerial Finance, 2008.
4. Turing, A.M., Computing machinery and intelligence, in Parsing the turing test. 2009, Springer. p. 23-65.
5. Hutson, M., How researchers are teaching AI to learn like a child. Science, 2018. 10.
6. Minsky, M. and S.A. Papert, Perceptrons: An introduction to computational geometry. 2017: MIT press.
7. Silver, D., et al., Mastering the game of Go with deep neural networks and tree search. nature, 2016. 529(7587): p. 484-489.
8. Aldridge, I., High-frequency trading: a practical guide to algorithmic strategies and trading systems. Vol. 604. 2013: John Wiley & Sons.
9. Lai, Z.-R., et al., Reweighted price relative tracking system for automatic portfolio optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018. 50(11): p. 4349-4361.
10. Rundo, F., et al., Grid trading system robot (gtsbot): A novel mathematical algorithm for trading fx market. Applied Sciences, 2019. 9(9): p. 1796.
11. Wen, Q., et al., Automatic stock decision support system based on box theory and SVM algorithm. Expert systems with Applications, 2010. 37(2): p. 1015-1022.
12. Deng, S. and A. Sakurai. Short-term foreign exchange rate trading based on the support/resistance level of Ichimoku Kinkohyo. in 2014 International Conference on Information Science, Electronics and Electrical Engineering. 2014. IEEE.
13. Ye, A., et al. Developing sustainable trading strategies using directional changes with high frequency data. in 2017 IEEE International Conference on Big Data (Big Data). 2017. IEEE.
14. Krauss, C., Statistical arbitrage pairs trading strategies: Review and outlook. Journal of Economic Surveys, 2017. 31(2): p. 513-545.
15. Poterba, J.M. and L.H. Summers, Mean reversion in stock prices: Evidence and implications. Journal of financial economics, 1988. 22(1): p. 27-59.
16. Hurst, B., Y.H. Ooi, and L.H. Pedersen, A century of evidence on trend-following investing. The Journal of Portfolio Management, 2017. 44(1): p. 15-29.
17. Kampouridis, M. and F.E. Otero, Evolving trading strategies using directional changes. Expert Systems with Applications, 2017. 73: p. 145-160.
18. MacKinlay, A.C., Event studies in economics and finance. Journal of economic literature, 1997. 35(1): p. 13-39.
19. Hautsch, N. and R. Huang, On the dark side of the market: identifying and analyzing hidden order placements. Available at SSRN 2004231, 2012.
20. Pardo, A. and R. Pascual, On the hidden side of liquidity. The European Journal of Finance, 2012. 18(10): p. 949-967.
21. DuPoly, A. The Expert4x, No stop, Hedged, Grid Trading System and The Hedged, Multi-Currency, Forex Trading System. 2008
22. Jegadeesh, N., Evidence of predictable behavior of security returns. The Journal of finance, 1990. 45(3): p. 881-898.
23. Jegadeesh, N., Seasonality in stock price mean reversion: Evidence from the US and the UK. The Journal of Finance, 1991. 46(4): p. 1427-1444.
24. Li, B., et al., PAMR: Passive aggressive mean reversion strategy for portfolio selection. Machine learning, 2012. 87(2): p. 221-258.
25. Kennedy, J. and R. Eberhart. Particle swarm optimization. in Proceedings of ICNN'95-international conference on neural networks. 1995. IEEE.
26. Yeh, W.-C., A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems. Expert Systems with Applications, 2009. 36(5): p. 9192-9200.
27. Yeh, W.-C., Novel swarm optimization for mining classification rules on thyroid gland data. Information Sciences, 2012. 197: p. 65-76.
28. Yeh, W.-C., W.-W. Chang, and Y.Y. Chung, A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method. Expert Systems with Applications, 2009. 36(4): p. 8204-8211.
29. Huang, C.-L., A particle-based simplified swarm optimization algorithm for reliability redundancy allocation problems. Reliability Engineering & System Safety, 2015. 142: p. 221-230.
30. Yeh, W.-C., Orthogonal simplified swarm optimization for the series–parallel redundancy allocation problem with a mix of components. Knowledge-Based Systems, 2014. 64: p. 1-12.
31. Yeh, W.-C., A novel boundary swarm optimization method for reliability redundancy allocation problems. Reliability Engineering & System Safety, 2019. 192: p. 106060.
32. Huang, W., et al., Neural networks in finance and economics forecasting. International Journal of Information Technology & Decision Making, 2007. 6(01): p. 113-140.
33. Qi, S., et al., The exploration of internet finance by using neural network. Journal of Computational and Applied Mathematics, 2020. 369: p. 112630.
34. Siami-Namini, S. and A.S. Namin, Forecasting economics and financial time series: ARIMA vs. LSTM. arXiv preprint arXiv:1803.06386, 2018.
35. Cao, J., Z. Li, and J. Li, Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and its Applications, 2019. 519: p. 127-139.
36. Russell, S. and P. Norvig, Artificial intelligence: a modern approach. 2002.
37. Deng, L. and D. Yu, Deep learning: methods and applications. Foundations and trends in signal processing, 2014. 7(3–4): p. 197-387.
38. McCulloch, W.S. and W. Pitts, A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 1943. 5(4): p. 115-133.
39. Rosenblatt, F., The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 1958. 65(6): p. 386.
40. Werbos, P., Beyond regression:" new tools for prediction and analysis in the behavioral sciences. Ph. D. dissertation, Harvard University, 1974.
41. Wang, S.-C., Artificial neural network, in Interdisciplinary computing in java programming. 2003, Springer. p. 81-100.
42. Leshno, M., et al., Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural networks, 1993. 6(6): p. 861-867.
43. Rumelhart, D.E., G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors. nature, 1986. 323(6088): p. 533-536.
44. Ruder, S., An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016.
45. Medsker, L.R. and L. Jain, Recurrent neural networks. Design and Applications, 2001. 5: p. 64-67.
46. Graves, A., Long short-term memory. Supervised sequence labelling with recurrent neural networks, 2012: p. 37-45.
47. Dunis, C.L. and A. Nathani, Quantitative trading of gold and silver using nonlinear models. Neural Network World, 2007. 17(2): p. 93.
48. Bloch, D.A., Recipe for quantitative trading with machine learning. Available at SSRN 3232143, 2018.
49. Ta, V.-D., C.-M. Liu, and D.A. Tadesse, Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 2020. 10(2): p. 437.
50. Jia, W., et al. Quantitative trading on stock market based on deep reinforcement learning. in 2019 International Joint Conference on Neural Networks (IJCNN). 2019. IEEE.
51. Liu, Y., et al. Adaptive quantitative trading: An imitative deep reinforcement learning approach. in Proceedings of the AAAI conference on artificial intelligence. 2020.