diff --git a/examples/data_analysis/data_analysis.ipynb b/examples/data_analysis/data_analysis.ipynb
new file mode 100644
index 00000000..5455ecc2
--- /dev/null
+++ b/examples/data_analysis/data_analysis.ipynb
@@ -0,0 +1,137 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from vnpy.trader.constant import Exchange,Interval\n",
+    "from data_analysis import DataAnalysis\n",
+    "from datetime import datetime\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "herramiento = DataAnalysis()\n",
+    "herramiento.load_history(    \n",
+    "    symbol=\"XBTUSD\", \n",
+    "    exchange=Exchange.BITMEX, \n",
+    "    interval=Interval.MINUTE, \n",
+    "    start=datetime(2019, 9, 1), \n",
+    "    end=datetime(2019, 10, 30),\n",
+    "    rate = 8/10000,\n",
+    "    index_3to1 = [\"ATR\",\"CCI\"],\n",
+    "    index_1to1 = [\"STDDEV\",\"SMA\"],\n",
+    "    index_2to2 = [\"AROON\"],\n",
+    "    index_2to1 = [\"AROONOSC\"],\n",
+    "    index_4to1 = [\"BOP\"],\n",
+    "    window_index=30,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data = herramiento.base_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herramiento.show_chart(data[:1500], boll_wide=2.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# 多时间周期分析\n",
+    "intervals = [\"5min\",\"15min\",\"30min\",\"1h\",\"2h\",\"4h\"]\n",
+    "herramiento.multi_time_frame_analysis(intervals=intervals)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/data_analysis/data_analysis.py b/examples/data_analysis/data_analysis.py
new file mode 100644
index 00000000..086636fa
--- /dev/null
+++ b/examples/data_analysis/data_analysis.py
@@ -0,0 +1,357 @@
+from datetime import datetime
+import warnings
+
+import numpy as np
+import pandas as pd
+from pandas import DataFrame
+import matplotlib.pyplot as plt
+from statsmodels.stats.diagnostic import acorr_ljungbox
+from statsmodels.tsa.stattools import adfuller as ADF
+from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
+import talib
+
+from vnpy.trader.constant import Exchange, Interval
+from vnpy.trader.database import database_manager
+warnings.filterwarnings("ignore")
+
+
+class DataAnalysis:
+
+    def __init__(self):
+        """"""
+        self.symbol = ""
+        self.exchange = None
+        self.interval = None
+        self.start = None
+        self.end = None
+        self.rate = 0.0
+
+        self.window_volatility = 20
+        self.window_index = 20
+
+        self.orignal = pd.DataFrame()
+
+        self.index_1to1 = []
+        self.index_2to2 = []   
+        self.index_3to1 = []
+        self.index_2to1 = []
+        self.index_4to1 = []
+        self.intervals = []
+
+        self.results = {}
+
+    def load_history(
+        self,
+        symbol: str, 
+        exchange: Exchange, 
+        interval: Interval, 
+        start: datetime, 
+        end: datetime,
+        rate: float = 0.0,
+        index_1to1: list = None,
+        index_2to2: list = None,
+        index_3to1: list = None,
+        index_2to1: list = None,
+        index_4to1: list = None,
+        window_index: int = 20,
+        window_volatility: int = 20,
+
+    ):
+        """""" 
+        output("开始加载历史数据")
+        
+        self.window_volatility = window_volatility
+        self.window_index = window_index
+        self.rate = rate
+        self.index_1to1 = index_1to1
+        self.index_2to2 = index_2to2
+        self.index_3to1 = index_3to1
+        self.index_2to1 = index_2to1
+        self.index_4to1 = index_4to1
+
+        # Load history data from database  
+        bars = database_manager.load_bar_data(    
+            symbol=symbol, 
+            exchange=exchange, 
+            interval=interval, 
+            start=start, 
+            end=end,
+
+        )
+
+        output(f"历史数据加载完成，数据量：{len(bars)}")
+        
+        # Generate history data in DataFrame
+        t = []
+        o = []
+        h = []
+        l = []
+        c = []
+        v = []
+
+        for bar in bars:
+            time = bar.datetime
+            open_price = bar.open_price
+            high_price = bar.high_price
+            low_price = bar.low_price
+            close_price = bar.close_price
+            volume = bar.volume
+            
+            t.append(time)
+            o.append(open_price)
+            h.append(high_price)
+            l.append(low_price)
+            c.append(close_price)  
+            v.append(volume)
+
+        self.orignal["open"] = o
+        self.orignal["high"] = h
+        self.orignal["low"] = l
+        self.orignal["close"] = c
+        self.orignal["volume"] = v
+        self.orignal.index = t
+   
+    def base_analysis(self, df: DataFrame = None):
+        """"""
+        if df is None:
+            df = self.orignal
+       
+        if df is None:
+            output("数据为空，请输入数据")
+
+        close_price = df["close"]
+
+        output("第一步:画出行情图，检查数据断点")
+        
+        close_price.plot(figsize=(20, 8), title="close_price")
+        plt.show()
+    
+        random_test(close_price)
+        stability_test(close_price)
+        autocorrelation_test(close_price)
+
+        self.relative_volatility_analysis(df)
+        self.growth_analysis(df)
+
+        self.calculate_index(df)
+
+        return df
+
+    def relative_volatility_analysis(self, df: DataFrame = None):
+        """
+        相对波动率
+        """
+        output("第五步：相对波动率分析")
+        df["volatility"] = talib.ATR(
+            np.array(df["high"]),
+            np.array(df["low"]),
+            np.array(df["close"]),
+            self.window_volatility
+        )
+
+        df["fixed_cost"] = df["close"] * self.rate
+        df["relative_vol"] = df["volatility"] - df["fixed_cost"]
+
+        df["relative_vol"].plot(figsize=(20, 6), title="relative volatility")
+        plt.show()
+
+        df["relative_vol"].hist(bins=200, figsize=(20, 6), grid=False)
+        plt.show()
+
+        statitstic_info(df["relative_vol"])  
+
+    def growth_analysis(self, df: DataFrame = None):
+        """
+        百分比K线变化率
+        """
+        output("第六步：变化率分析")
+        df["pre_close"] = df["close"].shift(1).fillna(0)
+        df["g%"] = 100 * (df["close"] - df["pre_close"]) / df["close"]
+
+        df["g%"].plot(figsize=(20, 6), title="growth", ylim=(-5, 5))
+        plt.show()
+
+        df["g%"].hist(bins=200, figsize=(20, 6), grid=False)
+        plt.show()
+        
+        statitstic_info(df["g%"])  
+
+    def calculate_index(self, df: DataFrame = None):
+        """"""
+        output("第七步：计算相关技术指标，返回DataFrame\n")
+
+        if self.index_1to1:
+            for i in self.index_1to1:
+                func = getattr(talib, i)
+                df[i] = func(
+                    np.array(df["close"]), 
+                    self.window_index
+                )
+
+        if self.index_3to1:
+            for i in self.index_3to1:
+                func = getattr(talib, i)
+                df[i] = func(        
+                    np.array(df["high"]),
+                    np.array(df["low"]),
+                    np.array(df["close"]),
+                    self.window_index
+                )
+                
+        if self.index_2to2:
+            for i in self.index_2to2:
+                func = getattr(talib, i)
+                result_down, result_up = func(
+                    np.array(df["high"]),
+                    np.array(df["low"]),
+                    self.window_index
+                )
+                up = i + "_UP"
+                down = i + "_DOWN"
+                df[up] = result_up
+                df[down] = result_down
+        
+        if self.index_2to1:
+            for i in self.index_2to1:
+                func = getattr(talib, i)
+                df[i] = func(
+                    np.array(df["high"]),
+                    np.array(df["low"]),
+                    self.window_index
+                )
+
+        if self.index_4to1:
+            for i in self.index_4to1:
+                func = getattr(talib, i)
+                df[i] = func(  
+                    np.array(df["open"]),      
+                    np.array(df["high"]),
+                    np.array(df["low"]),
+                    np.array(df["close"]),
+                )
+             
+        return df
+
+    def multi_time_frame_analysis(self, intervals: list = None, df: DataFrame = None):
+        """"""
+        if not intervals:
+            output("请输入K线合成周期")
+            return
+
+        if df is None:
+            df = self.orignal
+
+        if df is None:
+            output("请先加载数据")
+            return
+
+        for interval in intervals: 
+            output("------------------------------------------------")  
+            output(f"合成{interval}周期K先并开始数据分析")
+              
+            data = pd.DataFrame()
+            data["open"] = df["open"].resample(interval, how="first")
+            data["high"] = df["high"].resample(interval, how="max")
+            data["low"] = df["low"].resample(interval, how="min")
+            data["close"] = df["close"].resample(interval, how="last")
+            data["volume"] = df["volume"].resample(interval, how="sum")
+
+            result = self.base_analysis(data)
+            self.results[interval] = result
+
+    def show_chart(self, data, boll_wide):
+        """"""      
+        data["boll_up"] = data["SMA"] + data["STDDEV"] * boll_wide
+        data["boll_down"] = data["SMA"] - data["STDDEV"] * boll_wide
+
+        up_signal = []
+        down_signal = []
+        len_data = len(data["close"]) 
+        for i in range(1, len_data):
+            if data.iloc[i]["close"] > data.iloc[i]["boll_up"]and data.iloc[i-1]["close"] < data.iloc[i - 1]["boll_up"]:
+                up_signal.append(i)
+
+            elif data.iloc[i]["close"] < data.iloc[i]["boll_down"] and data.iloc[i-1]["close"] > data.iloc[i - 1]["boll_down"]:
+                down_signal.append(i)
+
+        fig = plt.figure(figsize=(20, 8))
+        close = data["close"]
+        plt.plot(close, lw=1)
+        plt.plot(close, '^', markersize=5, color='r', label='UP signal', markevery=up_signal)
+        plt.plot(close, 'v', markersize=5, color='g', label='DOWN signal', markevery=down_signal)
+        plt.plot(data["boll_up"], lw=0.5, color="r")
+        plt.plot(data["boll_down"], lw=0.5, color="g")
+        plt.legend()
+        plt.show()
+
+        data["ATR"].plot(figsize=(20, 3), title="ATR")
+        plt.show()
+
+
+def random_test(close_price):
+    """"""
+    acorr_result = acorr_ljungbox(close_price, lags=1)
+    p_value = acorr_result[1]
+    if p_value < 0.05:
+        output("第二步：随机性检验：非纯随机性")
+    else:
+        output("第二步：随机性检验：纯随机性")
+    output(f"白噪声检验结果:{acorr_result}\n")
+
+
+def stability_test(close_price):
+    """"""
+    statitstic = ADF(close_price)
+    t_s = statitstic[1]
+    t_c = statitstic[4]["10%"]
+
+    if t_s > t_c:
+        output("第三步：平稳性检验：存在单位根，时间序列不平稳")
+    else:
+        output("第三步：平稳性检验：不存在单位根，时间序列平稳")
+
+    output(f"ADF检验结果：{statitstic}\n")
+
+
+def autocorrelation_test(close_price):
+    """"""
+    output("第四步：画出自相关性图，观察自相关特性")
+
+    plot_acf(close_price, lags=60)
+    plt.show()
+
+    plot_pacf(close_price, lags=60).show()
+    plt.show()
+
+
+def statitstic_info(df):
+    """"""
+    mean = round(df.mean(), 4)
+    median = round(df.median(), 4)    
+    output(f"样本平均数：{mean}, 中位数: {median}")
+
+    skew = round(df.skew(), 4)
+    kurt = round(df.kurt(), 4)
+
+    if skew == 0:
+        skew_attribute = "对称分布"
+    elif skew > 0:
+        skew_attribute = "分布偏左"
+    else:
+        skew_attribute = "分布偏右"
+        
+    if kurt == 0:
+        kurt_attribute = "正态分布"
+    elif kurt > 0:
+        kurt_attribute = "分布陡峭"
+    else:
+        kurt_attribute = "分布平缓"
+    
+    output(f"偏度为：{skew}，属于{skew_attribute}；峰度为：{kurt}，属于{kurt_attribute}\n")
+
+
+def output(msg):
+    """
+    Output message of backtesting engine.
+    """
+    print(f"{datetime.now()}\t{msg}")