Stock-Market-Trend-Analysis-Using-HMM-LSTM

Introduction

The hidden Markov model (HMM) is a signal prediction model which has been used to predict economic regimes and stock prices. This project intends to achieve the goal of applying machine learning algrithms into stock market. The long short term memory model (LSTM) ensures that the previous information can continue to propagate backwards without disappearing as the hidden layer continuously superimposes the input sequence under the new time state.Our main purpose is to predict the ups and downs of one stock by using HMM-LSTM.
See details in our paper: PAPER

Process

Using data from 2007-2018 in China's A share stock market, including daily price and trade volume and over 200 types of feature, we divided them into 8 types of features and make 8 HMMs. Then combined them together to predict ups and downs of stock price the next day. During which, we used GMM and XGBoost to fit the emission matrix B of continuous HMMs and used LSTM to find a better connection of X and Y. Moreover, an useful method of labeling called the reiple barrier method is well used to find relationship between hidden states and the trends of stock price.

  #行情因子
  feature_col = ['closePrice', 'turnoverVol', 'highestPrice', 'lowestPrice']
  
  #质量类因子，描述资产负债，周转，运营，盈利，成本费用等指标
  type_zhiliang = ['AccountsPayablesTDays','AccountsPayablesTRate','AccountsPayablesTRate','ARTDays','ARTDays','ARTDays','BLEV',',BondsPayableToAsset','BondsPayableToAsset','CashRateOfSales','CashToCurrentLiability','CurrentAssetsRatio','CurrentRatio','DebtEquityRatio','DebtEquityRatio','DebtsAssetRatio','EBITToTOR','EquityFixedAssetRatio','EquityToAsset','EquityTRate','FinancialExpenseRate','FixAssetRatio','FixedAssetsTRate','GrossIncomeRatio','IntangibleAssetRatio','InventoryTDays','InventoryTRate','LongDebtToAsset','LongDebtToWorkingCapital','LongTermDebtToAsset','MLEV','NetProfitRatio','NOCFToOperatingNI','NonCurrentAssetsRatio','NPToTOR','OperatingExpenseRate','OperatingProfitRatio','OperatingProfitToTOR','OperCashInToCurrentLiability','QuickRatio','ROA','ROA5','ROE','ROE5','SalesCostRatio','SaleServiceCashToOR','TaxRatio','TotalAssetsTRate','TotalProfitCostRatio','CFO2EV','ACCA','DEGM']
   
  # 描述收益与风险
  type_shouyifengxian = ['CMRA','DDNBT','DDNCR','DDNSR','DVRAT','HBETA','HSIGMA','TOBT','Skewness','BackwardADJ']
   
  # 描述市值市盈市净
  type_jiazhi = ['CTOP','CTP5','ETOP','ETP5','LCAP','LFLO','PB','PCF','PE','PS','FY12P','SFY12P','TA2EV','ASSI']
   
  #情绪类，描述心理，换手率，动态买卖，成交量，人气，意愿，大盘趋势
  type_qingxu = ['DAVOL10','DAVOL20','DAVOL5','MAWVAD','PSY','RSI','VOL10','VOL120','VOL20','VOL240','VOL5','VOL60','WVAD','ADTM','ATR14','QTR6','SBM','STM','OBV','OBV6','TVMA20','TVMA6','TVSTD20','TVSTD6','VDEA','VDIFF','VEMA10','WEMA12','VEMA26','VEMA5','VMACD','VOSC','VR','VROC12','VROC6','VSTD10','VSTD20','ACD6','ACD20','AR','BR','ARBR','NVI','PVI','JDQS20','KlingerOscillator','MoneyFlow20','Volatility']
   
  #技术指标类，平均移动线，计算周期，动态移动，差异
  type_zhibiao = ['MassIndex','SwingIndex','minusDI','plusDI','ChaikinVolatility','ChaikinOscillator','DownRVI','BollUp','BollDown','DHILO','EMA10','EMA120','EMA20','EMA5','EMA60','EA10','EA120','EA20','EA5','EA60','MFI','ILLIQUIDITY','MACD','KDJ_K','KDJ_D','KDJ_J','UpRVI','RVI','DBCD','ASI','EMV12','EMV6','ADX','ADXR','MTM','MTMMA','UOS','EMA12','EMA26','BBI','TEMA10','Ulcer10','Hurst','Ulcer5','TEMA5','CR20','Elder','DilutedEPS','EPS']
   
  #动量类因子，描述平均移动，圆滑曲线，收益，增长率，未来趋势预测
  type_dongliang = ['REVS10','REVS10','REVS5','RSTR12','RSTR24','DAREC','GREC','DAREV','GREV','DASREV','GSREV','EARNMOM','FiftyTwoWeekHigh','BIAS10','BIAS20','BIAS5','BIAS60','CCI10''CCI20','CCI5','CCI88','ROC6','ROC20','SRMI','ChandeSD','ChandeSU','CMO','ARC','AD','AD20','AD6','CoppockCurve','Aroon','AroonDown','AroonUp','DEA','DIFF','DDI','DIZ','DIF','PVT','PCT6','PVT12','TRIX5','TRIX10','MA10RegressCoeff12','MA10RegressCoeff6','PLRC6','PLRC12','APBMA','BBIC','MA10Close','BearPower','RC12','RC24']
   
  #增长类，计算增长率
  type_zengzhang = ['EGRO','FinancingCashGrowRate','InvestCashGrowRate','NetAssetGrowRate','NetProfitGrowRate','NPParentCompanyGrowRate','OperatingProfitGrowRate','OperatingRevenueGrowRate','OperCashGrowRate','SUE','TotalAssetGrowRate','TotalProfitGrowRate','REC','FEARNG','FSALESG','SUOI']

Experiment with 4 different models:

GMM-HMM
XGB-HMM
GMM-HMM-LSTM
XGB-HMM-LSTM

Compared with the results:

train_set

test_set

iteration_process

Accuracy
GMM-HMM-LSTM performs 76.1612738%
XGB-HMM-LSTM performs 80.6991611%

Contribution

Contributors

Junbang Huo
Yulin Wu
Jinge Wu

Institutions

AI&FintechLab of Likelihood Technology
Sun Yat-sen University
Xi'an Jiaotong-Liverpool University

Acknowledgement

We would like to say thanks to Maxwell Liu from ShingingMidas Private Fund, Jiahui Wu and Xingyu Fu from Sun Yat-sen University for their generous guidance throughout the project

Set up

Python Version

Modules needed

numpy
pandas
hmmlearn
xgboost
...

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