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+.vscode
+**.pdf
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diff --git a/courses/sec2_chap1_deciphering_the_markets_with_technical_analysis.md b/courses/sec2_chap1_deciphering_the_markets_with_technical_analysis.md
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+# Chap 2. Deciphering the Markets with Technical Analysis (使用技术分析来解密市场)
+
+本章目标：
+
+* 介绍流行的技术分析方法
+* 展示如何用来分析市场数据
+
+创造交易策略的基础：我们假设市场会不断重复自身。通过分析过往记录，市场数据的技术分析可分为两块：
+
+* chart pattern (图表): 此类技术分析通过识别交易的模式并预测他们何时会重复。比较应用于算法交易
+* technical indicator (技术指标): 使用数学来预测金融市场走向。指标有很多但可被归纳为
+  * trend (趋势)
+  * momentum (动量)
+  * volume (体量)
+  * volatility (波动)
+  * support and resistance (支撑与阻力)
+
+## 1. Designing a trading strategy based on trend- and momentum-based indicator (设计一个基于趋势和动能的指标)
+
+“趋势交易策略关注速度，动能交易策略关注加速度”
+
+* 趋势交易:
+  * 在分析历史价格数据时，如果价格不断上涨一定时间（天），我们就开多头仓位
+* 动能交易:
+  * 基于过往行为的强度来挂单
+  * price momentum 是一个价格动作的量
+  * 逻辑：an asset price with a strong movement in a given direction will keep going in the same direction in the future.
+
+### 1.1 Support and resistance indicators （支撑线、阻力线）
+
+* 当价格下跌到一定程度时，会有集中的需求导致下跌停止。当价格上涨到一定程度时，会有集中性的供给导致价格无法上升
+* Support line = 价格下跌的下限
+* Resistance line = 价格上涨的上限
+* 利用了买低卖高的市场心理
+
+![](sources/sec2/supportresistanceline.png)
+
+* 获取2015-07-01 至2018-01-01的GOOGLE的股票价格。
+* 画出了 support line (红线), resistence line （绿线)
+* 蓝线表示数据积累点 (200天)
+
+基于这个简单的技术分析，我们可以定出策略，在200天的数据积累后（蓝色虚线）：
+
+* 股价高至 resistence line 后，挂空头仓位 (short the stock).
+* 股价低至 support line 后，挂多头仓位 (long the stock).
+
+实际效果：
+
+* 2016-08 后，GOOG 股价触发 resistance line, 算法开始持续做空 GOOGLE, 损失惨重。
+
+分析：
+
+* 即便 support & resistance indicator 有经济学逻辑，实际中需要对其进行矫正。例如：移动 support/resistance line.
+
+改进（增加特性）:
+
+* 使用 rolling window 滚动窗口
+* 数股价触及支撑线或阻力线的数量
+* 加入 tolerance margin， 从而把支撑/阻力线的空间缩小（如下图）
+
+![](../img/2_1.jpg)
+
+方案选择（增加两种参数）：
+
+* 价格触及 supporting/resistance line 的最少次数
+* 定一个 tolorance margine
+
+```
+                  price  sup_tolerance  res_tolerance  sup_count  res_count         sup          res  position  signal
+Date                                                                                                                  
+2013-12-31   558.262512            NaN            NaN        NaN        NaN         NaN          NaN       NaN     NaN
+2014-01-02   554.481689            NaN            NaN        NaN        NaN         NaN          NaN       NaN     NaN
+2014-01-03   550.436829            NaN            NaN        NaN        NaN         NaN          NaN       NaN     NaN
+2014-01-06   556.573853            NaN            NaN        NaN        NaN         NaN          NaN       NaN     NaN
+2014-01-07   567.303589            NaN            NaN        NaN        NaN         NaN          NaN       NaN     NaN
+...                 ...            ...            ...        ...        ...         ...          ...       ...     ...
+2017-12-22  1060.119995    1014.371997     1061.44801        NaN        NaN  998.679993  1077.140015       0.0     1.0
+2017-12-26  1056.739990    1014.371997     1061.44801        NaN        NaN  998.679993  1077.140015       0.0     1.0
+2017-12-27  1049.369995    1014.371997     1061.44801        NaN        NaN  998.679993  1077.140015       0.0     1.0
+2017-12-28  1048.140015    1014.371997     1061.44801        NaN        NaN  998.679993  1077.140015       0.0     1.0
+2017-12-29  1046.400024    1014.371997     1061.44801        NaN        NaN  998.679993  1077.140015       0.0     1.0
+```
+
+代码完成：
+
+1. 在规定的滚动时间窗口内（默认20天）基于过去的数据计算支撑线、阻力线。
+2. 基于支撑线、阻力线，计算了20%的区域。
+3. 用diff()计算了下单的时间
+4. 当价格低于支撑线，到达支撑区间后（或者高于阻力线，到达阻力区间），我们会挂多头仓位（空头仓位）
+
+![](sources/sec2/supportresistanceline_improved.png)
+
+交易策略：
+
+* 当价格进入支撑线区域后连续两天，就可以买多仓
+* 价格进入阻力线区域后连续两天，买空仓
+
+## 2. Creating trading signals based on fundamental technical analysis (基于基本技术分析的交易算法)
+
+常用技术分析方法(指标):
+
+* Simple Moving Average (SMA) 简单移动平均线
+* Exponential Moving Average (EMA) 指数移动平均线
+* Absolute Price Oscillator (APO) 绝对价格震荡器
+* Moving Average Convergence Divergence (MACD) 指数平滑移动平均线
+* Bollinger Bands (BBANDS) 布林线指标
+* Relative Strength Indicator (RSI) 相对强弱和是
+
+* EMA是非常常用的时间序列技术指标
+* 类似于SMA,但是对价格的权重不是平均的
+* 背后的概念：
+  * 若模型使用“时间约近，权重越高”：追求更新的信息
+  * 若模型使用“时间越远，权重越高”：认为 longer-term trends 含有更多信息
+* 时间窗口：
+  * 越短：EMA 对新数据的反映越大; i.e. **Fast EMA**
+  * 越长：EMA 对新数据的反映月满; i.e. **Slow EMA**
+
+#### 基于旧EMA计算新EMA
+
+$$EMA = (P - EMA_{old})\times \mu + EMA_{old}$$
+
+or 
+
+$$EMA = P \times \mu + (1-\mu) \times EMA_{old}$$
+
+where:
+
+* $P$ = 新的（当今）价格
+* $/pu$ = **smoothing factor**; 新的价格数据的 weight factor (权重), 经常被设置为 $\frac{2}{(n+1)}$
+* $N$ = 时间
+* $EMA_{old}$ = 之前的EMA数值
+
+![](sources/sec2/ema.png)
+
+* EMA 和 SMA 一样具有平滑作用，因此减少了噪音
+* EMA 中具有的 $\mu$ & $N$，可以让我们控制新进数据的权重。可以让我们通过不同的 $\mu$ & $N$ 建立不同的EMA
+
+### 2.3 绝对价格震荡器 (APO)
+
+* APO 为一种技术指标：通过价格的移动平均数值来捕捉价格在短期内的背离
+* 计算方法：计算 Fast EMA 和 Slow EMA 时间的价差。
+* 背后的逻辑：试图衡量 $EMA_{fast}$ 背离 $EMA_{slow}$ 多远。更大的背离标志着两种情况
+  * 金融资产价格开始出现趋势或者准备爆发
+  * 金融资产价格已经偏离平衡，要么是被低估或者高估
+
+$$APO = EMA_{fast} - EMA_{slow}$$
+
+下面案例使用了 $N=2/11$ 和 $N=2/41$ 的 APO
+
+![](sources/sec2/apo.png)
+
+* $EMA_{fast}$ 对新价格的的波动性更敏感，而 $SMA_{slow}$ 衰退得更加慢
+* 当股价价格向上爆发的时候 APO 为正；而且爆发程度越强 APO 绝对值越高
+
+这个信号可以用来做交易策略
+
+### 2.4 指数平滑移动平均线 (MACD)
+
+* MACD 是一种基于APO的指标
+* 计算方法：
+  * 在计算 APO 后，我们对其再做一次 EMA，从而得到一个MACD信号 ($MACD_{Signal}$)
+  * 或者，在$MACD_{Signal}$基础上生成histogram
+
+$$MACD = EMA_{Fast} - EMA_{Slow}$$
+$$MACD_{Signal} = EMA_{MACD}$$
+$$MACD_{Histogram} = MACD-MACD_{Signal}$$
+
+一个成功的 $MACD_{signal}$ 可以捕获金融产品的方向、趋势和时间长度
+
+![](sources/sec2/macd.png)
+
+* $EMA_{MACD}$ 在 MACD(APO)上在加了一层平滑。
+* $MACD_{Histogram}$ 捕获了两个点：
+  * 趋势开始或者回归的时间
+  * 当$MACD_{Histogram}$值在反转正负以后，趋势持续的强度 (magnitude of lasting trends when $MACD_{Histogram}$ values stay positive or negative after reversing signs)
+
+### 2.5 布林线指标 (BBANDS)
+
+Bollinger Bands:
+
+* 一种著名的技术分析指标，由 John Bollinger 发明
+* 计算方式（类似 6-sigma）：
+  * 1. 使用一个时间窗口，计算 moving average (SMA, EMA 都可)
+  * 2. 计算这个时间窗口的 standard deviation
+  * 3. Upper Band = moving average 加上 standard deviation 的倍数
+  * 4. Lower Band = moving average 减去 standard deviation 的倍数
+* BBAND 代表了对价格波动性的预估。若价格到了BBAND之外，那就是一个交易信号。
+
+$$BBAND_{Middle} = SMA_{n-periods}$$
+
+* Lower Bollinger band $BBAND_{Middle}$  是前n个时段的SMA (.e.g. 过去n天)
+
+$$BBAND_{Upper} = BBAND_{Middle} + (\beta * \delta)$$
+$$BBAND_{Lower} = BBAND_{Middle} - (\beta * \delta)$$
+
+* $\delta$ 是standard deviation
+* $\beta$ 是 standard deviation factor，由我们选择; $\beta$ 越大，BBAND越大
+
+![](../bbands.png)
+
+### 2.6 相对强弱指标 (RSI)
+
+* RSI通过比较一段时期内的平均收盘涨数和平均收盘跌数来分析市场买沽盘的意向和实力，从而作出未来市场的走势
+* RSI在1978年6月由Wells Wider创制的一种通过特定时期内股价的变动情况计算市场买卖力量对比，来判断股票价格内部本质强弱、推测价格未来的变动方向的技术指标。
+
+> RSI＝[上升平均数÷(上升平均数＋下跌平均数)]×100; 
+> 上升平均数是在某一段日子里升幅数的平均而下跌平均数则是在同一段日子里跌幅数的平均。
+
+计算方法, 在lookback时间段内:
+
+1. 计算magnitude of average of gains/losses over the period
+
+$$AbsoluteGainOverPeriod = Price - PreviousPrice$$
+
+$$AbsoluteLossOverPeriod = PreviousPrice - Price$$
+
+$$RelativeStrength(RS) = \frac{\frac{\sum AbsoluteGainsOverLastNPeriods}{n}}{\frac{\sum AbsoluteLossesOverLastNPeriods}{n}}$$
+
+可被简化为:
+
+$$RelativeStrength(RS) = \frac{\sum AbsoluteGainsOverLastNPeriods}{\sum AbsoluteLossesOverLastNPeriods}$$
+
+$$RelativeStrengthIndicator(RSI) = 100 - \frac{100}{(1+RS)}$$
+
+![](sources/sec2/rsi.png)
+
+* 上图使用了20天作为时间区域来计算上升平均数与下降平均数
+* 上图说明了上升平均数基本上高于下降平均数，这与GOOG成功的股价直接相关
+
+### 2.7 标准差 (STDEV)
+
+**Standard deviation** = **STDEV**, 是金融资产价格波动性的体现，可被用于结合其他的指标。
+
+计算方法：
+
+1. 先计算 variance:
+
+$$\sigma ^2 = \frac{\sum^n_{i=1}(P_i - SMA)^2}{n}$$
+
+SMA = simple moving average over n time periods
+
+2. 计算标准差:
+
+$$\sigma = \sqrt{\sigma^2}$$
+
+![](sources/sec2/stdev.png)
+
+### 2.8 动量指标 (MOM)
+
+MOM 为一个对价格移动的速度和量的衡量指标。可用作 trend/breakout-based trading algorithms.
+
+$$MOM = Price_i - Price_{t-n}$$
+
+* $Price_t$ 为在t时间的价格
+* $Price_{t-n}$ 在t时间之前n个时间片段的价格
+
+MOM的含义：
+
+* 持续为正的MOM表示上升趋势
+
+![](sources/sec2/momentum.png)
+
+上图所示:
+
+* MOM揭示了相比于20天前的股价，股价何时会有大幅变动
+
+## 3. Implementing advanced concepts in trading instruments
+
+### 3.1 季节性
+
+金融产品价格会有季节性（周期性）：每周，每月，每个假期, 例如下图
+
+![](sources/sec2/seasonality.png)
+
+* GOOG的从2001到2018的每月平均股价在10月有明显偏高
+
+### 3.2 Time Series analysis
+
+时间序列分析因为较为advanced,因此这里暂时略过
+
diff --git a/courses/sources/chap2.ipynb b/courses/sources/chap2.ipynb
new file mode 100644
index 0000000..470aeca
--- /dev/null
+++ b/courses/sources/chap2.ipynb
@@ -0,0 +1,407 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chap 2. Deciphering the Markets with Technical Analysis\n",
+    "\n",
+    "### 1.1 Support and Resistance Line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pandas_datareader import data\n",
+    "\n",
+    "start_date = '2014-01-01'\n",
+    "end_date = '2018-01-01'\n",
+    "SRC_DATA_FILENAME = 'goog_data.pkl'\n",
+    "\n",
+    "try:\n",
+    "    goog_data2 = pd.read_pickle(SRC_DATA_FILENAME)\n",
+    "except FileNotFoundError:\n",
+    "    goog_data2 = data.DataReader('GOOG', 'yahoo', start_date, end_date)\n",
+    "    goog_data2.to_pickle(SRC_DATA_FILENAME)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "把GOOGLE数据下载下来"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "                   High          Low         Open        Close      Volume  \\\nDate                                                                         \n2015-07-17   674.468018   645.000000   649.000000   672.929993  11164900.0   \n2015-07-20   668.880005   653.010010   659.239990   663.020020   5860900.0   \n2015-07-21   673.000000   654.299988   655.210022   662.299988   3377200.0   \n2015-07-22   678.640015   659.000000   660.890015   662.099976   3929300.0   \n2015-07-23   663.630005   641.000000   661.270020   644.280029   3029100.0   \n...                 ...          ...          ...          ...         ...   \n2017-12-22  1064.199951  1059.439941  1061.109985  1060.119995    755100.0   \n2017-12-26  1060.119995  1050.199951  1058.069946  1056.739990    760600.0   \n2017-12-27  1058.369995  1048.050049  1057.390015  1049.369995   1271900.0   \n2017-12-28  1054.750000  1044.770020  1051.599976  1048.140015    837100.0   \n2017-12-29  1049.699951  1044.900024  1046.719971  1046.400024    887500.0   \n\n              Adj Close  \nDate                     \n2015-07-17   672.929993  \n2015-07-20   663.020020  \n2015-07-21   662.299988  \n2015-07-22   662.099976  \n2015-07-23   644.280029  \n...                 ...  \n2017-12-22  1060.119995  \n2017-12-26  1056.739990  \n2017-12-27  1049.369995  \n2017-12-28  1048.140015  \n2017-12-29  1046.400024  \n\n[620 rows x 6 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Open</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>Adj Close</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2015-07-17</th>\n      <td>674.468018</td>\n      <td>645.000000</td>\n      <td>649.000000</td>\n      <td>672.929993</td>\n      <td>11164900.0</td>\n      <td>672.929993</td>\n    </tr>\n    <tr>\n      <th>2015-07-20</th>\n      <td>668.880005</td>\n      <td>653.010010</td>\n      <td>659.239990</td>\n      <td>663.020020</td>\n      <td>5860900.0</td>\n      <td>663.020020</td>\n    </tr>\n    <tr>\n      <th>2015-07-21</th>\n      <td>673.000000</td>\n      <td>654.299988</td>\n      <td>655.210022</td>\n      <td>662.299988</td>\n      <td>3377200.0</td>\n      <td>662.299988</td>\n    </tr>\n    <tr>\n      <th>2015-07-22</th>\n      <td>678.640015</td>\n      <td>659.000000</td>\n      <td>660.890015</td>\n      <td>662.099976</td>\n      <td>3929300.0</td>\n      <td>662.099976</td>\n    </tr>\n    <tr>\n      <th>2015-07-23</th>\n      <td>663.630005</td>\n      <td>641.000000</td>\n      <td>661.270020</td>\n      <td>644.280029</td>\n      <td>3029100.0</td>\n      <td>644.280029</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2017-12-22</th>\n      <td>1064.199951</td>\n      <td>1059.439941</td>\n      <td>1061.109985</td>\n      <td>1060.119995</td>\n      <td>755100.0</td>\n      <td>1060.119995</td>\n    </tr>\n    <tr>\n      <th>2017-12-26</th>\n      <td>1060.119995</td>\n      <td>1050.199951</td>\n      <td>1058.069946</td>\n      <td>1056.739990</td>\n      <td>760600.0</td>\n      <td>1056.739990</td>\n    </tr>\n    <tr>\n      <th>2017-12-27</th>\n      <td>1058.369995</td>\n      <td>1048.050049</td>\n      <td>1057.390015</td>\n      <td>1049.369995</td>\n      <td>1271900.0</td>\n      <td>1049.369995</td>\n    </tr>\n    <tr>\n      <th>2017-12-28</th>\n      <td>1054.750000</td>\n      <td>1044.770020</td>\n      <td>1051.599976</td>\n      <td>1048.140015</td>\n      <td>837100.0</td>\n      <td>1048.140015</td>\n    </tr>\n    <tr>\n      <th>2017-12-29</th>\n      <td>1049.699951</td>\n      <td>1044.900024</td>\n      <td>1046.719971</td>\n      <td>1046.400024</td>\n      <td>887500.0</td>\n      <td>1046.400024</td>\n    </tr>\n  </tbody>\n</table>\n<p>620 rows × 6 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "goog_data = goog_data2.tail(620)\n",
+    "lows = goog_data['Low']\n",
+    "highs = goog_data['High']\n",
+    "goog_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "fig = plt.figure()\n",
+    "ax1 = fig.add_subplot(111, ylabel='Google price in $')\n",
+    "highs.plot(ax=ax1, color='c', lw=2.)\n",
+    "lows.plot(ax=ax1, color='y', lw=2.)\n",
+    "\n",
+    "# Return the first 200 rows using pandas.DataFrame.head(200)\n",
+    "plt.hlines(highs.head(200).max(), lows.index.values[0], lows.index.values[-1], linewidth=2, color='g') \n",
+    "plt.hlines(lows.head(200).min(), lows.index.values[0], lows.index.values[-1], linewidth=2, color='r')\n",
+    "plt.axvline(linewidth=2, color='b', x=lows.index.values[200], linestyle=':')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* 获取2015-07-01 至2018-01-01的GOOGLE的股票价格。\n",
+    "* 画出了 support line (红线), resistence line （绿线)\n",
+    "* 蓝线表示数据积累点 (200天)\n",
+    "\n",
+    "基于这个简单的技术分析，我们可以定出策略，在200天的数据积累后（蓝色虚线）：\n",
+    "\n",
+    "* 股价高至 resistence line 后，挂空头仓位 (short the stock).\n",
+    "* 股价低至 support line 后，挂多头仓位 (long the stock).\n",
+    "\n",
+    "实际效果：\n",
+    "\n",
+    "* 2016-08 后，GOOG 股价触发 resistance line, 算法开始持续做空 GOOGLE, 损失惨重。\n",
+    "\n",
+    "分析： \n",
+    "\n",
+    "* 即便 support & resistance indicator 有经济学逻辑，实际中需要对其进行矫正。例如：移动 support/resistance line.\n",
+    "\n",
+    "改进（增加特性）:\n",
+    "\n",
+    "* 使用 rolling window 滚动窗口\n",
+    "* 数股价触及支撑线或阻力线的数量\n",
+    "* 加入 tolerance margin， 从而把支撑/阻力线的空间缩小（如下图）\n",
+    "\n",
+    "![](../../img/2_1.jpg)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "方案选择（增加两种参数）：\n",
+    "\n",
+    "* 价格触及 supporting/resistance line 的最少次数\n",
+    "* 定一个 tolorance margine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "File data found...reading GOOG data\n"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pandas_datareader import data\n",
+    "\n",
+    "start_date = '2014-01-01'\n",
+    "end_date = '2018-01-01'\n",
+    "SRC_DATA_FILENAME = 'goog_data.pkl'\n",
+    "\n",
+    "try:\n",
+    "    goog_data = pd.read_pickle(SRC_DATA_FILENAME)\n",
+    "    print('File data found...reading GOOG data')\n",
+    "except FileNotFoundError:\n",
+    "    print('File not found...downloading the GOOG data')\n",
+    "    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)\n",
+    "    goog_data.to_pickle(SRC_DATA_FILENAME)\n",
+    "\n",
+    "goog_data_signal = pd.DataFrame(index=goog_data.index)\n",
+    "goog_data_signal['price'] = goog_data['Adj Close']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def trading_support_resistance(data, bin_width=20):\n",
+    "\n",
+    "    data['sup_tolerance'] = pd.Series(np.zeros(len(data))) # tolerance of support line\n",
+    "    data['res_tolerance'] = pd.Series(np.zeros(len(data))) # tolerance of resistance line\n",
+    "\n",
+    "    data['sup_count'] = pd.Series(np.zeros(len(data))) # number of hitting support line\n",
+    "    data['res_count'] = pd.Series(np.zeros(len(data)))\n",
+    "\n",
+    "    data['sup'] = pd.Series(np.zeros(len(data))) # support line value (constant)\n",
+    "    data['res'] = pd.Series(np.zeros(len(data)))\n",
+    "\n",
+    "    data['position'] = pd.Series(np.zeros(len(data)))\n",
+    "    data['signal'] = pd.Series(np.zeros(len(data)))\n",
+    "\n",
+    "    in_support = 0\n",
+    "    in_resistance = 0\n",
+    "\n",
+    "    for x in range((bin_width-1) + bin_width, len(data)):\n",
+    "        data_section = data[x-bin_width : x+1] # get data within rolling window\n",
+    "        \n",
+    "        support_level = min(data_section['price'])\n",
+    "        resistance_level = max(data_section['price'])\n",
+    "        range_level = resistance_level - support_level\n",
+    "\n",
+    "        data['res'][x] = resistance_level\n",
+    "        data['sup'][x] = support_level\n",
+    "\n",
+    "        data['sup_tolerance'][x] = support_level + 0.2*range_level # 20% tolorance\n",
+    "        data['res_tolerance'][x] = resistance_level - 0.2*range_level\n",
+    "\n",
+    "        if (data['price'][x] >= data['res_tolerance'][x]) and (data['price'][x] <= data['res'][x]):\n",
+    "            # if price is within resistance tolerance region\n",
+    "            in_resistance += 1\n",
+    "            data['res_count'][x] = in_resistance\n",
+    "        elif (data['price'][x] <= data['sup_tolerance'][x]) and (data['price'][x] >= data['sup'][x]):\n",
+    "            # price is within support tolerance region\n",
+    "            in_support += 1\n",
+    "            data['sup_count'][x] = in_support\n",
+    "        else:\n",
+    "            # if not within any region, clear count\n",
+    "            in_support = 0\n",
+    "            in_resistance = 0\n",
+    "\n",
+    "        if in_resistance > 2:\n",
+    "            # If enter resistance region twice\n",
+    "            data['signal'][x] = 1\n",
+    "        elif in_support > 2:\n",
+    "            data['signal'][x] = 0\n",
+    "        else:\n",
+    "            data['signal'][x] = data['signal'][x-1]\n",
+    "    \n",
+    "    data['position'] = data['signal'].diff()\n",
+    "\n",
+    "trading_support_resistance(goog_data_signal)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "上述代码完成了\n",
+    "\n",
+    "* 在规定的滚动时间窗口内（默认20天）基于过去的数据计算支撑线、阻力线。\n",
+    "* 基于支撑线、阻力线，计算了20%的区域。\n",
+    "* 用diff()计算了下单的时间\n",
+    "* 当价格低于支撑线，到达支撑区间后（或者高于阻力线，到达阻力区间），我们会挂多头仓位（空头仓位）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "                  price  sup_tolerance  res_tolerance  sup_count  res_count  \\\nDate                                                                          \n2013-12-31   558.262512            NaN            NaN        NaN        NaN   \n2014-01-02   554.481689            NaN            NaN        NaN        NaN   \n2014-01-03   550.436829            NaN            NaN        NaN        NaN   \n2014-01-06   556.573853            NaN            NaN        NaN        NaN   \n2014-01-07   567.303589            NaN            NaN        NaN        NaN   \n...                 ...            ...            ...        ...        ...   \n2017-12-22  1060.119995    1014.371997     1061.44801        NaN        NaN   \n2017-12-26  1056.739990    1014.371997     1061.44801        NaN        NaN   \n2017-12-27  1049.369995    1014.371997     1061.44801        NaN        NaN   \n2017-12-28  1048.140015    1014.371997     1061.44801        NaN        NaN   \n2017-12-29  1046.400024    1014.371997     1061.44801        NaN        NaN   \n\n                   sup          res  position  signal  \nDate                                                   \n2013-12-31         NaN          NaN       NaN     NaN  \n2014-01-02         NaN          NaN       NaN     NaN  \n2014-01-03         NaN          NaN       NaN     NaN  \n2014-01-06         NaN          NaN       NaN     NaN  \n2014-01-07         NaN          NaN       NaN     NaN  \n...                ...          ...       ...     ...  \n2017-12-22  998.679993  1077.140015       0.0     1.0  \n2017-12-26  998.679993  1077.140015       0.0     1.0  \n2017-12-27  998.679993  1077.140015       0.0     1.0  \n2017-12-28  998.679993  1077.140015       0.0     1.0  \n2017-12-29  998.679993  1077.140015       0.0     1.0  \n\n[1008 rows x 9 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>price</th>\n      <th>sup_tolerance</th>\n      <th>res_tolerance</th>\n      <th>sup_count</th>\n      <th>res_count</th>\n      <th>sup</th>\n      <th>res</th>\n      <th>position</th>\n      <th>signal</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2013-12-31</th>\n      <td>558.262512</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-02</th>\n      <td>554.481689</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>550.436829</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>556.573853</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>567.303589</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2017-12-22</th>\n      <td>1060.119995</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-26</th>\n      <td>1056.739990</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-27</th>\n      <td>1049.369995</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-28</th>\n      <td>1048.140015</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-29</th>\n      <td>1046.400024</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1008 rows × 9 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "goog_data_signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "                  price  sup_tolerance  res_tolerance  sup_count  res_count  \\\nDate                                                                          \n2013-12-31   558.262512            NaN            NaN        NaN        NaN   \n2014-01-02   554.481689            NaN            NaN        NaN        NaN   \n2014-01-03   550.436829            NaN            NaN        NaN        NaN   \n2014-01-06   556.573853            NaN            NaN        NaN        NaN   \n2014-01-07   567.303589            NaN            NaN        NaN        NaN   \n...                 ...            ...            ...        ...        ...   \n2017-12-22  1060.119995    1014.371997     1061.44801        NaN        NaN   \n2017-12-26  1056.739990    1014.371997     1061.44801        NaN        NaN   \n2017-12-27  1049.369995    1014.371997     1061.44801        NaN        NaN   \n2017-12-28  1048.140015    1014.371997     1061.44801        NaN        NaN   \n2017-12-29  1046.400024    1014.371997     1061.44801        NaN        NaN   \n\n                   sup          res  position  signal  \nDate                                                   \n2013-12-31         NaN          NaN       NaN     NaN  \n2014-01-02         NaN          NaN       NaN     NaN  \n2014-01-03         NaN          NaN       NaN     NaN  \n2014-01-06         NaN          NaN       NaN     NaN  \n2014-01-07         NaN          NaN       NaN     NaN  \n...                ...          ...       ...     ...  \n2017-12-22  998.679993  1077.140015       0.0     1.0  \n2017-12-26  998.679993  1077.140015       0.0     1.0  \n2017-12-27  998.679993  1077.140015       0.0     1.0  \n2017-12-28  998.679993  1077.140015       0.0     1.0  \n2017-12-29  998.679993  1077.140015       0.0     1.0  \n\n[1008 rows x 9 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>price</th>\n      <th>sup_tolerance</th>\n      <th>res_tolerance</th>\n      <th>sup_count</th>\n      <th>res_count</th>\n      <th>sup</th>\n      <th>res</th>\n      <th>position</th>\n      <th>signal</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2013-12-31</th>\n      <td>558.262512</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-02</th>\n      <td>554.481689</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>550.436829</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>556.573853</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>567.303589</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2017-12-22</th>\n      <td>1060.119995</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-26</th>\n      <td>1056.739990</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-27</th>\n      <td>1049.369995</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-28</th>\n      <td>1048.140015</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-29</th>\n      <td>1046.400024</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1008 rows × 9 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "goog_data_signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
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109.434088 168.03436 \nL 109.642699 168.676754 \nL 109.85131 171.382698 \nL 110.059921 164.913295 \nL 110.685755 166.613224 \nL 110.894366 165.016315 \nL 111.102977 164.640585 \nL 111.311588 165.437519 \nL 111.520199 166.74957 \nL 112.146033 166.270801 \nL 112.354644 167.782865 \nL 112.563255 167.234403 \nL 112.771866 172.028116 \nL 112.980477 173.703806 \nL 113.606311 171.558434 \nL 113.814922 174.006819 \nL 114.023533 173.611672 \nL 114.232144 174.524978 \nL 114.440755 172.885656 \nL 115.066588 173.155343 \nL 115.2752 174.715866 \nL 115.483811 171.064533 \nL 115.692422 171.103923 \nL 115.901033 171.458438 \nL 116.526866 168.82827 \nL 116.735477 167.404093 \nL 117.1527 168.461609 \nL 117.987144 169.422169 \nL 118.195755 170.131235 \nL 118.404367 172.209935 \nL 118.612978 172.755356 \nL 118.821589 172.028116 \nL 119.864644 170.106996 \nL 120.281867 167.640436 \nL 120.9077 166.537466 \nL 121.116311 169.176739 \nL 121.324922 168.543431 \nL 121.533533 169.073701 \nL 121.742145 170.809997 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182.360975 \nL 136.970757 181.824622 \nL 137.179368 181.276179 \nL 137.387979 181.491306 \nL 137.805201 181.048905 \nL 138.431035 183.482138 \nL 138.639646 183.497289 \nL 138.848257 184.233617 \nL 139.056868 182.41854 \nL 139.265479 186.069892 \nL 139.891313 185.54871 \nL 140.099924 183.612419 \nL 140.308535 185.827484 \nL 140.517146 185.136593 \nL 140.725757 188.069793 \nL 141.351591 189.542447 \nL 141.560202 195.120984 \nL 141.977424 190.360597 \nL 142.186035 188.769771 \nL 142.811869 186.188063 \nL 143.02048 184.454808 \nL 143.229091 185.006293 \nL 143.646313 183.412444 \nL 144.272146 184.533589 \nL 144.480758 184.506327 \nL 144.689369 185.724446 \nL 145.106591 186.206238 \nL 145.732424 189.52125 \nL 145.941035 193.13017 \nL 146.149647 193.390761 \nL 146.358258 192.911992 \nL 146.566869 194.884632 \nL 147.192702 195.981547 \nL 148.027147 191.275711 \nL 148.861591 191.633267 \nL 149.487425 181.618583 \nL 150.113258 183.05487 \nL 150.53048 190.693914 \nL 150.739091 190.493929 \nL 150.947703 183.263951 \nL 151.573536 185.09418 \nL 151.782147 184.863883 \nL 151.990758 186.827435 \nL 152.40798 184.330572 \nL 153.033814 185.291132 \nL 153.242425 182.530665 \nL 153.451036 182.824591 \nL 153.868258 178.873249 \nL 154.702703 180.742868 \nL 154.911314 181.694341 \nL 155.119925 180.733781 \nL 155.328536 181.921596 \nL 155.95437 184.054839 \nL 156.162981 182.788224 \nL 156.788814 176.027936 \nL 157.623259 171.40996 \nL 157.83187 171.491782 \nL 158.040481 170.897865 \nL 158.249092 173.214429 \nL 158.874925 172.861417 \nL 159.083537 177.055148 \nL 159.292148 178.215702 \nL 159.500759 176.903651 \nL 159.70937 179.385343 \nL 160.335203 177.206665 \nL 160.543814 178.318741 \nL 160.752426 175.694619 \nL 160.961037 176.152172 \nL 161.169648 175.434019 \nL 161.795481 175.9037 \nL 162.004092 172.455365 \nL 162.212703 175.911267 \nL 162.421315 177.006671 \nL 162.629926 179.076265 \nL 163.255759 177.958144 \nL 163.672981 180.827713 \nL 163.881593 182.957915 \nL 164.924648 182.506426 \nL 165.133259 181.115575 \nL 165.550482 181.600408 \nL 166.176315 181.854926 \nL 166.384926 184.515414 \nL 166.802148 183.482138 \nL 167.010759 186.436535 \nL 167.636593 183.003369 \nL 167.845204 183.430619 \nL 168.262426 179.482317 \nL 168.471037 174.009842 \nL 169.514093 178.395241 \nL 169.722704 181.962415 \nL 169.931315 181.792261 \nL 170.557149 180.917177 \nL 170.974371 185.94892 \nL 171.391593 181.695046 \nL 172.017427 182.46073 \nL 172.226038 184.484369 \nL 172.434649 184.308132 \nL 172.64326 181.640337 \nL 172.851871 183.022861 \nL 173.477704 183.493822 \nL 173.686316 181.956351 \nL 173.894927 181.37599 \nL 174.103538 180.391525 \nL 174.312149 181.120768 \nL 175.146593 183.487739 \nL 175.355205 181.218002 \nL 175.563816 181.221025 \nL 175.772427 183.551553 \nL 176.39826 182.980317 \nL 176.606871 181.403345 \nL 176.815482 181.059995 \nL 177.232705 183.180849 \nL 177.858538 185.155862 \nL 178.067149 185.198405 \nL 178.27576 182.159924 \nL 179.318816 185.04344 \nL 179.736038 184.417513 \nL 179.944649 182.147776 \nL 180.153261 182.159924 \nL 180.779094 181.704151 \nL 180.987705 181.008346 \nL 181.613538 183.679164 \nL 182.239372 186.769295 \nL 182.447983 187.076185 \nL 182.865205 186.198059 \nL 183.69965 186.362149 \nL 183.908261 185.705826 \nL 184.116872 188.194344 \nL 184.325483 187.024536 \nL 184.534094 184.153167 \nL 185.159928 179.163985 \nL 185.368539 174.742998 \nL 185.57715 175.010386 \nL 185.785761 169.045845 \nL 185.994372 140.763654 \nL 186.620206 143.774781 \nL 187.037428 144.054334 \nL 187.45465 155.764636 \nL 188.289095 154.415549 \nL 188.497706 153.221428 \nL 188.706317 153.020878 \nL 188.914928 155.141751 \nL 189.540761 153.44019 \nL 189.749372 154.035739 \nL 189.957984 149.620816 \nL 190.166595 149.95506 \nL 190.375206 152.197461 \nL 191.001039 152.674505 \nL 191.20965 144.455398 \nL 191.418261 144.826102 \nL 191.626873 145.771065 \nL 191.835484 145.567492 \nL 192.461317 144.428062 \nL 192.669928 145.868299 \nL 192.878539 144.418937 \nL 193.08715 148.694083 \nL 193.295762 159.131278 \nL 194.130206 168.374334 \nL 194.338817 154.227165 \nL 194.547428 151.495573 \nL 194.75604 153.692389 \nL 195.381873 157.378069 \nL 195.590484 163.594809 \nL 195.799095 158.566107 \nL 196.216317 162.7106 \nL 197.050762 158.468892 \nL 197.259373 159.058358 \nL 197.676595 155.093125 \nL 198.302429 155.861869 \nL 198.51104 152.246069 \nL 198.719651 151.990847 \nL 198.928262 149.888204 \nL 199.136873 154.035739 \nL 199.762707 152.154918 \nL 199.971318 156.028982 \nL 200.179929 156.129257 \nL 200.38854 155.084019 \nL 200.597151 159.286244 \nL 201.222984 164.475957 \nL 201.431596 164.451663 \nL 201.640207 160.364901 \nL 201.848818 159.492859 \nL 202.057429 154.746752 \nL 202.891874 149.116437 \nL 203.309096 151.024612 \nL 203.517707 149.672484 \nL 204.14354 148.742709 \nL 204.352151 147.032043 \nL 204.560763 147.378434 \nL 204.769374 144.163715 \nL 204.977985 144.023938 \nL 205.603818 142.838942 \nL 206.02104 149.976332 \nL 206.229652 147.187008 \nL 206.438263 131.930785 \nL 207.064096 128.655293 \nL 207.272707 129.958814 \nL 207.689929 127.397376 \nL 207.898541 129.253884 \nL 208.524374 126.124252 \nL 208.732985 125.805215 \nL 209.150207 123.043227 \nL 209.358819 122.280565 \nL 209.984652 124.975697 \nL 210.193263 123.9335 \nL 210.401874 121.78225 \nL 210.610485 123.04931 \nL 210.819096 127.373063 \nL 211.44493 123.739032 \nL 211.653541 124.851127 \nL 211.862152 120.384555 \nL 212.070763 120.867682 \nL 212.279374 115.340683 \nL 212.905208 115.529068 \nL 213.113819 117.868684 \nL 213.32243 117.908186 \nL 213.739652 117.26707 \nL 214.365486 119.594558 \nL 214.574097 112.168508 \nL 214.782708 113.584432 \nL 214.991319 116.574306 \nL 215.19993 112.238387 \nL 216.034375 113.587474 \nL 216.242986 116.856883 \nL 216.451597 117.510145 \nL 216.660208 120.727905 \nL 217.286041 118.023649 \nL 217.494653 119.351464 \nL 217.703264 114.887934 \nL 218.120486 120.594211 \nL 218.95493 117.346074 \nL 219.163542 117.251882 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130.997969 \nL 233.349098 133.216057 \nL 233.557709 126.823099 \nL 233.766321 126.810951 \nL 233.974932 128.764693 \nL 234.183543 129.229571 \nL 234.809376 134.009115 \nL 235.017987 134.370695 \nL 235.643821 124.389272 \nL 236.269654 123.274154 \nL 236.478265 123.930458 \nL 236.686876 121.572594 \nL 236.895487 121.059089 \nL 237.104099 121.113798 \nL 237.729932 119.749505 \nL 237.938543 120.156669 \nL 238.355765 121.812646 \nL 239.19021 122.350444 \nL 239.607432 117.185025 \nL 239.816043 118.880503 \nL 240.024654 117.373428 \nL 240.650488 118.777205 \nL 240.859099 121.053025 \nL 241.06771 118.655659 \nL 241.276321 120.299469 \nL 242.110766 121.569571 \nL 242.527988 116.823464 \nL 242.736599 116.373756 \nL 242.94521 114.61144 \nL 243.571044 112.29916 \nL 243.779655 116.151953 \nL 243.988266 116.534805 \nL 244.196877 114.568897 \nL 244.405488 126.835246 \nL 245.031321 125.50439 \nL 245.239933 130.065153 \nL 245.448544 130.764 \nL 245.657155 135.267032 \nL 246.491599 133.082363 \nL 246.70021 134.859886 \nL 246.908822 133.845025 \nL 247.117433 132.103981 \nL 247.326044 129.159692 \nL 247.951877 128.618833 \nL 248.160488 125.495284 \nL 248.3691 127.89265 \nL 248.786322 129.247801 \nL 249.412155 127.528029 \nL 249.620766 130.645514 \nL 249.829377 130.523967 \nL 250.037989 132.441248 \nL 250.2466 129.579004 \nL 250.872433 131.250169 \nL 251.081044 126.434164 \nL 251.289655 124.860233 \nL 251.498266 125.209666 \nL 251.706878 122.614809 \nL 252.541322 121.685034 \nL 252.749933 122.16206 \nL 252.958544 123.30149 \nL 253.167155 125.750506 \nL 253.792989 127.509798 \nL 254.0016 127.479403 \nL 254.210211 123.945647 \nL 254.418822 123.854496 \nL 254.627433 126.640797 \nL 255.461878 126.98717 \nL 255.670489 126.78968 \nL 255.8791 129.390619 \nL 256.087711 135.054353 \nL 256.713545 134.44968 \nL 257.130767 133.310249 \nL 257.339378 131.970287 \nL 257.547989 140.067848 \nL 258.173823 142.182619 \nL 258.382434 138.603298 \nL 258.591045 137.366633 \nL 259.008267 132.778515 \nL 259.842712 134.072911 \nL 260.051323 133.216057 \nL 260.259934 133.948342 \nL 260.468545 130.827815 \nL 261.094378 127.953405 \nL 261.302989 126.267051 \nL 261.511601 127.379146 \nL 261.720212 126.172859 \nL 261.928823 126.507103 \nL 262.971879 120.022975 \nL 263.18049 120.800826 \nL 263.389101 119.552014 \nL 264.223545 120.864641 \nL 264.640768 118.588821 \nL 264.849379 111.636774 \nL 265.475212 110.394027 \nL 265.683823 110.943991 \nL 265.892434 110.302876 \nL 266.101045 110.77992 \nL 266.309657 107.556095 \nL 266.93549 107.695853 \nL 267.144101 106.936233 \nL 267.561323 106.756973 \nL 267.769934 107.252247 \nL 268.395768 107.489239 \nL 268.604379 109.09963 \nL 268.81299 108.257984 \nL 269.230212 109.622259 \nL 269.856046 110.61583 \nL 270.064657 110.637101 \nL 270.273268 111.378492 \nL 270.69049 111.408888 \nL 271.316324 110.61583 \nL 271.733546 112.165467 \nL 272.150768 110.825486 \nL 272.985213 108.206316 \nL 273.193824 108.12429 \nL 273.402435 109.652636 \nL 273.611046 114.410909 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax1 = fig.add_subplot(111, ylabel='Google price in $')\n",
+    "goog_data_signal['sup'].plot(ax=ax1, color='g', lw=2.)\n",
+    "goog_data_signal['res'].plot(ax=ax1, color='b', lw=2.)\n",
+    "goog_data_signal['price'].plot(ax=ax1, color='r', lw=2.)\n",
+    "\n",
+    "ax1.plot(goog_data_signal.loc[goog_data_signal.position == 1.0].index,\n",
+    "        goog_data_signal.price[goog_data_signal.position == 1.0], '^', markersize=7, color='k', label='buy')\n",
+    "ax1.plot(goog_data_signal.loc[goog_data_signal.position  == -1.0].index,\n",
+    "        goog_data_signal.price[goog_data_signal.position == -1.0], 'v', markersize=7, color='k', label='sell')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "交易策略：\n",
+    "\n",
+    "* 当价格进入支撑线区域后连续两天，就可以买多仓\n",
+    "* 价格进入阻力线区域后连续两天，买空仓"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Creating trading signals based on fundamental technical analysis\n",
+    "\n",
+    "## SMA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "import statistics as stats\n",
+    "\n",
+    "time_period = 20 # number of days over which to average\n",
+    "history = [] # to track a history of prices\n",
+    "sma_values = [] \n",
+    "\n",
+    "for close_price in goog_data['Adj Close']:\n",
+    "    history.append(close_price)\n",
+    "    if len(history) > time_period:\n",
+    "        del(history[0])\n",
+    "    sma_values.append(stats.mean(history))\n",
+    "\n",
+    "goog_data = goog_data.assign(ClosePrice=pd.Series(goog_data['Adj Close'], index=goog_data.index))\n",
+    "goog_data = goog_data.assign(Simple20DayMovingAverage=pd.Series(sma_values, index=goog_data.index))\n",
+    "close_price = goog_data['ClosePrice']\n",
+    "sma = goog_data['Simple20DayMovingAverage']\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax1 = fig.add_subplot(111, ylabel='Google price in $')\n",
+    "close_price.plot(ax=ax1, color='g', lw=2., legend=True)\n",
+    "sma.plot(ax=ax1, color='r', lw=2., legend=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "20日SMA线消除了部分噪音"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/courses/sources/sec2/apo.png b/courses/sources/sec2/apo.png
new file mode 100644
index 0000000..eb6a06d
Binary files /dev/null and b/courses/sources/sec2/apo.png differ
diff --git a/courses/sources/sec2/apo.py b/courses/sources/sec2/apo.py
new file mode 100644
index 0000000..450b9ca
--- /dev/null
+++ b/courses/sources/sec2/apo.py
@@ -0,0 +1,75 @@
+import os
+import time
+import statistics
+from matplotlib.pyplot import legend, ylabel
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = dir_path+'/goog_data.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+close = goog_data_signal['price']
+
+exe_start_time = time.time()
+
+""" Calculate APO """
+
+num_periods_fast = 10 # time period for the fast EMA
+K_fast = 2/(num_periods_fast + 1)
+ema_fast = 0
+
+num_periods_slow = 40
+K_slow = 2/(num_periods_slow + 1)
+ema_slow = 0
+
+ema_fast_values = [] # Hold fast EMA values for visualization purposes
+ema_slow_values = [] # Hold slow EMA values for visualization purposes
+apo_values = []
+
+for close_price in close:
+    if (ema_fast == 0):
+        ema_fast = close_price
+        ema_slow = close_price
+    else:
+        ema_fast = (close_price - ema_fast) * K_fast + ema_fast
+        ema_slow = (close_price - ema_slow) * K_slow + ema_slow
+
+    ema_fast_values.append(ema_fast)
+    ema_slow_values.append(ema_slow)
+    apo_values.append(ema_fast - ema_slow)
+
+""" Visualizing """
+goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
+goog_data = goog_data.assign(FastExponential10DayMovingAverage=pd.Series(ema_fast_values, index=goog_data.index))
+goog_data = goog_data.assign(SlowExponential140DayMovingAverage=pd.Series(ema_slow_values, index=goog_data.index))
+goog_data = goog_data.assign(AbsolutePriceOscillator=pd.Series(apo_values, index=goog_data.index))
+close_price = goog_data['ClosePrice']
+ema_f = goog_data['FastExponential10DayMovingAverage']
+ema_s = goog_data['SlowExponential140DayMovingAverage']
+apo = goog_data['AbsolutePriceOscillator']
+
+import matplotlib.pyplot as plt
+fig = plt.figure()
+ax1 = fig.add_subplot(211, ylabel='Google price in $')
+close_price.plot(ax=ax1, color='g', lw=2., legend=True)
+ema_f.plot(ax=ax1, color='b', lw=2., legend=True)
+ema_s.plot(ax=ax1, color='r', lw=2., legend=True)
+ax2 = fig.add_subplot(212, ylabel='APO')
+apo.plot(ax=ax2, color='black', lw=2., legend=True)
+plt.savefig(dir_path + "/apo.png")
+plt.show()
+
diff --git a/courses/sources/sec2/bbands.png b/courses/sources/sec2/bbands.png
new file mode 100644
index 0000000..d00181c
Binary files /dev/null and b/courses/sources/sec2/bbands.png differ
diff --git a/courses/sources/sec2/bbands.py b/courses/sources/sec2/bbands.py
new file mode 100644
index 0000000..9032814
--- /dev/null
+++ b/courses/sources/sec2/bbands.py
@@ -0,0 +1,73 @@
+import os
+import time
+import statistics
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = dir_path + '/goog_data.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+close = goog_data_signal['price']
+exe_start_time = time.time()
+
+import statistics as stats
+import math as math
+
+time_period = 20 # history length for Simple Moving Average for middle band
+stdev_factor = 2 # Standard Deviation Scaling factor for the upper and lower bands
+
+history = []
+sma_values = []
+upper_band = []
+lower_band = []
+
+""" Calculate BBAND """
+
+for close_price in close:
+    history.append(close_price)
+    if len(history) > time_period:
+        del(history[0])
+
+    sma = stats.mean(history) # BBAND_Middle
+    sma_values.append(sma)
+
+    variance = 0
+
+    # Calculate variance using pure python
+    for hist_price in history:
+        variance = variance + ((hist_price - sma) ** 2)
+
+    stdev = math.sqrt(variance / len(history))
+    upper_band.append(sma + stdev_factor * stdev)
+    lower_band.append(sma - stdev_factor * stdev)
+
+""" Draw Gragh """
+goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
+goog_data = goog_data.assign(MiddleBollingerBand20DaySMA=pd.Series(sma_values, index=goog_data.index))
+goog_data = goog_data.assign(UpperBollingerBand20DaysSMA2StddevFactor=pd.Series(upper_band, index=goog_data.index))
+goog_data = goog_data.assign(LowerBollingerBand20DaysSMA2StddevFactor=pd.Series(lower_band, index=goog_data.index))
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+ax1 = fig.add_subplot(111, ylabel='Google price in $')
+goog_data['ClosePrice'].plot(ax=ax1, color='g', lw=2., legend=True)
+goog_data['MiddleBollingerBand20DaySMA'].plot(ax=ax1, color='b', lw=2., legend=True)
+goog_data['UpperBollingerBand20DaysSMA2StddevFactor'].plot(ax=ax1, color='y', lw=2., legend=True)
+goog_data['LowerBollingerBand20DaysSMA2StddevFactor'].plot(ax=ax1, color='r', lw=2., legend=True)
+plt.savefig(dir_path + '/bbands.png')
+plt.show()
\ No newline at end of file
diff --git a/courses/sources/sec2/ema.png b/courses/sources/sec2/ema.png
new file mode 100644
index 0000000..0cc8da2
Binary files /dev/null and b/courses/sources/sec2/ema.png differ
diff --git a/courses/sources/sec2/ema.py b/courses/sources/sec2/ema.py
new file mode 100644
index 0000000..e91290e
--- /dev/null
+++ b/courses/sources/sec2/ema.py
@@ -0,0 +1,58 @@
+import os
+import time
+import statistics
+from matplotlib.pyplot import ylabel
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = dir_path + '/goog_data.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+close = goog_data_signal['price']
+
+exe_start_time = time.time()
+
+""" Calculate EMA """
+
+num_periods = 20 # number of days over which to average
+K = 2/(num_periods + 1) # smoothing constant
+ema_p = 0
+ema_values = []
+
+for close_price in close:
+    if (ema_p == 0): 
+        # first observation, EMA = current price
+        ema_p = close_price
+    else:
+        ema_p = (close_price - ema_p) * K + ema_p
+
+    ema_values.append(ema_p)
+
+goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
+goog_data = goog_data.assign(Exponential120DayMovingAverage=pd.Series(ema_values, index=goog_data.index))
+close_price = goog_data['ClosePrice']
+ema = goog_data['Exponential120DayMovingAverage']
+
+""" Draw plot """
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+ax1 = fig.add_subplot(111, ylabel='Google price in $')
+close_price.plot(ax=ax1, color='g', lw=2., legend=True)
+ema.plot(ax=ax1, color='b', lw=2., legend=True)
+plt.savefig(dir_path + '/ema.png')
+plt.show()
\ No newline at end of file
diff --git a/courses/sources/sec2/goog_data.pkl b/courses/sources/sec2/goog_data.pkl
new file mode 100644
index 0000000..5aef72a
Binary files /dev/null and b/courses/sources/sec2/goog_data.pkl differ
diff --git a/courses/sources/sec2/goog_data_large.pkl b/courses/sources/sec2/goog_data_large.pkl
new file mode 100644
index 0000000..8c9b877
Binary files /dev/null and b/courses/sources/sec2/goog_data_large.pkl differ
diff --git a/courses/sources/sec2/macd.png b/courses/sources/sec2/macd.png
new file mode 100644
index 0000000..1a62c8a
Binary files /dev/null and b/courses/sources/sec2/macd.png differ
diff --git a/courses/sources/sec2/macd.py b/courses/sources/sec2/macd.py
new file mode 100644
index 0000000..d593651
--- /dev/null
+++ b/courses/sources/sec2/macd.py
@@ -0,0 +1,97 @@
+import os
+import time
+import statistics
+from matplotlib.pyplot import legend, ylabel
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = dir_path + '/goog_data.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+close = goog_data_signal['price']
+
+exe_start_time = time.time()
+
+""" Calculate MACD """
+
+num_periods_fast = 10 # fast EMA time period
+K_fast = 2/(num_periods_fast+1)
+ema_fast = 0
+
+num_periods_slow = 40 # slow EMA time period
+K_slow = 2/(num_periods_slow+1)
+ema_slow = 0
+
+num_periods_macd = 20 # MACD EMA time period
+K_macd = 2/(num_periods_macd+1)
+ema_macd=0
+
+ema_fast_values = []
+ema_slow_values = []
+macd_values = []
+macd_signal_values = []
+
+macd_histogram_values = []
+
+for close_price in close:
+    if (ema_fast == 0):
+        ema_fast = close_price
+        ema_slow = close_price
+    else:
+        ema_fast = (close_price - ema_fast) * K_fast + ema_fast
+        ema_slow = (close_price - ema_slow) * K_slow + ema_slow
+
+    ema_fast_values.append(ema_fast)
+    ema_slow_values.append(ema_slow)
+    macd = ema_fast - ema_slow # calculate MACD
+
+    # Based on APO(MACD), calculate EMA_MACD
+    if ema_macd == 0:
+        ema_macd = macd
+    else:
+        ema_macd = (macd - ema_macd) * K_slow + ema_macd
+    macd_values.append(macd)
+
+    macd_signal_values.append(ema_macd)
+    macd_histogram_values.append(macd - ema_macd)
+
+""" Visualization """
+# assign data back to goog_data to get index and aligned
+goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
+goog_data = goog_data.assign(Fast_EMA_110Days=pd.Series(ema_fast_values, index=goog_data.index))
+goog_data = goog_data.assign(Slow_EMA_140Days=pd.Series(ema_slow_values, index=goog_data.index))
+goog_data = goog_data.assign(MACD=pd.Series(macd_values, index=goog_data.index))
+goog_data = goog_data.assign(EMA_of_MACD_120Days=pd.Series(macd_signal_values, index=goog_data.index))
+goog_data = goog_data.assign(MACDHistogram=pd.Series(macd_histogram_values, index=goog_data.index))
+print(goog_data['MACDHistogram'])
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+ax1 = fig.add_subplot(311, ylabel='Google price in $')
+goog_data['ClosePrice'].plot(ax=ax1, color='g', lw=2., legend=True)
+goog_data['Fast_EMA_110Days'].plot(ax=ax1, color='b', lw=2., legend=True)
+goog_data['Slow_EMA_140Days'].plot(ax=ax1, color='r', lw=2., legend=True)
+ax2 = fig.add_subplot(312, ylabel='MACD')
+goog_data['MACD'].plot(ax=ax2, color='black', lw=2., legend=True)
+goog_data['EMA_of_MACD_120Days'].plot(ax=ax2, color='g', lw=2., legend=True)
+ax3 = fig.add_subplot(313, ylabel='MACD')
+goog_data['MACDHistogram'].plot(ax=ax3, color='r', kind='bar', legend=True, use_index=False)
+
+plt.savefig(dir_path + "/macd.png")
+plt.show()
\ No newline at end of file
diff --git a/courses/sources/sec2/momentum.png b/courses/sources/sec2/momentum.png
new file mode 100644
index 0000000..4c4264c
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diff --git a/courses/sources/sec2/momentum.py b/courses/sources/sec2/momentum.py
new file mode 100644
index 0000000..b1b2fa8
--- /dev/null
+++ b/courses/sources/sec2/momentum.py
@@ -0,0 +1,54 @@
+import os
+import time
+import statistics
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = dir_path + '/goog_data.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+close = goog_data_signal['price']
+exe_start_time = time.time()
+
+""" Calculate momentum """
+
+time_period = 20 # lookback period
+history = [] # history of observed prices to use in momentum calculation
+mom_values = [] # track momentum values for visualization purposes
+
+for close_price in close:
+    history.append(close_price)
+    if len(history) > time_period:
+        del(history[0])
+
+    mom = close_price - history[0]
+    mom_values.append(mom)
+
+goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
+goog_data = goog_data.assign(MomentumFromPrice20DaysAgo=pd.Series(mom_values, index=goog_data.index))
+
+""" Visualization """
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+ax1 = fig.add_subplot(211, ylabel='Google price in $')
+goog_data['ClosePrice'].plot(ax=ax1, color='g', lw=2., legend=True)
+ax2 = fig.add_subplot(212, ylabel='Momentum in $')
+goog_data['MomentumFromPrice20DaysAgo'].plot(ax=ax2, color='b', lw=2., legend=True)
+plt.savefig(dir_path + '/momentum.png')
+plt.show()
\ No newline at end of file
diff --git a/courses/sources/sec2/rsi.png b/courses/sources/sec2/rsi.png
new file mode 100644
index 0000000..09a6b80
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diff --git a/courses/sources/sec2/rsi.py b/courses/sources/sec2/rsi.py
new file mode 100644
index 0000000..d896b2b
--- /dev/null
+++ b/courses/sources/sec2/rsi.py
@@ -0,0 +1,81 @@
+import os
+import time
+import statistics
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = dir_path + '/goog_data.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+close = goog_data_signal['price']
+exe_start_time = time.time()
+
+import statistics as stats
+
+time_period = 20 # look back period to compute gains & losses
+
+gain_history = [] # history of gains over look back period (0 if no gain, magnitude of gain if gain)
+loss_history = [] # history of losses over look back period (0 if no loss, magnitude of loss if loss)
+avg_gain_values = [] # track avg gains for visualization purposes
+avg_loss_values = []
+
+rsi_values = [] # track computed RSI values
+
+last_price = 0 # current_price - last_price > 0 => gain ; current_price - last_price < 0 => loss.
+
+for close_price in close:
+    if last_price == 0:
+        last_price = close_price
+
+    gain_history.append(max(0, close_price - last_price))
+    loss_history.append(max(0, last_price - close_price))
+    last_price = close_price
+
+    if len(gain_history) > time_period:
+        del(gain_history[0])
+        del(loss_history[0])
+
+    avg_gain = stats.mean(gain_history) # average gain over lookback period
+    avg_loss = stats.mean(loss_history) # average loss over lookback period
+    avg_gain_values.append(avg_gain)
+    avg_loss_values.append(avg_loss)
+
+    rs = 0
+    if avg_loss > 0: # to avoid division by 0, which is undefined
+        rs = avg_gain /avg_loss
+    rsi = 100 - (100/(1+rs))
+    rsi_values.append(rsi)
+
+""" Data Visualization """
+
+goog_data = goog_data.assign(ClosePrice=pd.Series(close,index=goog_data.index))
+goog_data = goog_data.assign(RelativeStrengthAvgGainOver20Days=pd.Series(avg_gain_values, index=goog_data.index))
+goog_data = goog_data.assign(RelativeStrengthAvgLossOver20Days=pd.Series(avg_loss_values, index=goog_data.index))
+goog_data = goog_data.assign(RelativeStrengthIndicatorOver20Days=pd.Series(rsi_values, index=goog_data.index))
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+ax1 = fig.add_subplot(311, ylabel='Google price in $')
+goog_data['ClosePrice'].plot(ax=ax1, color='black', lw=2., legend=True)
+ax2 = fig.add_subplot(312, ylabel='RS')
+goog_data['RelativeStrengthAvgGainOver20Days'].plot(ax=ax2, color='g', lw=2., legend=True)
+goog_data['RelativeStrengthAvgLossOver20Days'].plot(ax=ax2, color='r', lw=2., legend=True)
+ax3 = fig.add_subplot(313, ylabel='RSI')
+goog_data['RelativeStrengthIndicatorOver20Days'].plot(ax=ax3, color='b', lw=2., legend=True)
+plt.savefig(dir_path + '/rsi.png')
+plt.show()
\ No newline at end of file
diff --git a/courses/sources/sec2/seasonality.png b/courses/sources/sec2/seasonality.png
new file mode 100644
index 0000000..1725cb3
Binary files /dev/null and b/courses/sources/sec2/seasonality.png differ
diff --git a/courses/sources/sec2/seasonality.py b/courses/sources/sec2/seasonality.py
new file mode 100644
index 0000000..8c86303
--- /dev/null
+++ b/courses/sources/sec2/seasonality.py
@@ -0,0 +1,58 @@
+import os
+import time
+import statistics
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+start_date = '2001-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = dir_path + '/goog_data_large.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+close = goog_data_signal['price']
+exe_start_time = time.time()
+
+""" Calculate mean of each month's return """
+
+goog_monthly_return = goog_data['Adj Close'].pct_change().groupby(
+    [goog_data['Adj Close'].index.year, goog_data['Adj Close'].index.month]).mean()
+print(goog_monthly_return)
+
+goog_monthly_return_list = []
+
+for i in range(len(goog_monthly_return)):
+    goog_monthly_return_list.append({'month':goog_monthly_return.index[i][1],'monthly_return': goog_monthly_return[i]})
+
+goog_monthly_return_list=pd.DataFrame(goog_monthly_return_list,\
+    columns=('month','monthly_return'))
+print(goog_monthly_return_list)
+
+goog_monthly_return_list.boxplot(column='monthly_return', by='month')
+
+""" Visulization """
+
+import matplotlib.pyplot as plt
+
+ax = plt.gca()
+labels = [item.get_text() for item in ax.get_xticklabels()]
+print(labels)
+labels = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
+ax.set_xticklabels(labels)
+ax.set_ylabel('GOOG return')
+plt.tick_params(axis='both', which='major', labelsize=7)
+plt.title("GOOG Monthly return 2001-2018")
+plt.suptitle("")
+plt.savefig(dir_path + '/seasonality.png')
+plt.show()
\ No newline at end of file
diff --git a/courses/sources/sec2/sma.png b/courses/sources/sec2/sma.png
new file mode 100644
index 0000000..c966f91
Binary files /dev/null and b/courses/sources/sec2/sma.png differ
diff --git a/courses/sources/sec2/sma.py b/courses/sources/sec2/sma.py
new file mode 100644
index 0000000..58c0570
--- /dev/null
+++ b/courses/sources/sec2/sma.py
@@ -0,0 +1,64 @@
+import os
+import time
+import statistics
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = 'goog_data.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+
+exe_start_time = time.time()
+
+import statistics as stats
+
+time_period = 20 # number of days over which to average
+history = [] # to track a history of prices
+sma_values = [] # to track simple moving average values
+
+for close_price in goog_data_signal['price']:
+    history.append(close_price)
+    if len(history) > time_period:
+         # we remove oldest price because we only average over last 'time_period' prices
+         # i.e. keep the latest 20 day window
+        del(history[0])
+    sma_values.append(stats.mean(history))
+    # The first 20 sma of this data will not be mean of 20 values
+    
+print("--- algo1: %s seconds ---" % (time.time() - exe_start_time))
+
+exe_start_time = time.time()
+
+sma_values_improved = goog_data_signal.rolling(window=20, win_type='boxcar').mean()
+
+print("--- algo2: %s seconds ---" % (time.time() - exe_start_time))
+
+goog_data = goog_data.assign(ClosePrice=pd.Series(goog_data_signal['price'], index=goog_data.index))
+goog_data = goog_data.assign(Simple20DayMovingAverage=pd.Series(sma_values, index=goog_data.index))
+goog_data = goog_data.assign(Simple20DayMovingAverage_pd = sma_values_improved)
+close_price = goog_data['ClosePrice']
+sma = goog_data['Simple20DayMovingAverage']
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+ax1 = fig.add_subplot(111, ylabel='Google price in $')
+close_price.plot(ax=ax1, color='g', lw=2., legend=True)
+sma.plot(ax=ax1, color='r', lw=2., legend=True)
+goog_data['Simple20DayMovingAverage_pd'].plot(ax=ax1, color='b', lw=1., legend=True)
+plt.savefig(dir_path+'/sma.png')
+plt.show()
diff --git a/courses/sources/sec2/sma_improved.py b/courses/sources/sec2/sma_improved.py
new file mode 100644
index 0000000..7f2a4d1
--- /dev/null
+++ b/courses/sources/sec2/sma_improved.py
@@ -0,0 +1,58 @@
+# Same method as in sma.py, used pandas.DataFrame.rolling for faster calculation
+
+import time
+import statistics
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+exe_start_time = time.time()
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = 'goog_data.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+
+import statistics as stats
+
+time_period = 20 # number of days over which to average
+history = [] # to track a history of prices
+sma_values = [] # to track simple moving average values
+
+
+
+goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
+goog_data = goog_data.assign(Simple20DayMovinigAverage=pd.Series(result, index=goog_data.index))
+print(result)
+
+
+# for close_price in close:
+#     history.append(close_price)
+#     if len(history) > time_period: # we remove oldest price because we only average over last 'time_period' prices
+#         del(history[0])
+#     sma_values.append(stats.mean(history))
+
+# goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
+# goog_data = goog_data.assign(Simple20DayMovingAverage=pd.Series(sma_values, index=goog_data.index))
+# close_price = goog_data['ClosePrice']
+# sma = goog_data['Simple20DayMovingAverage']
+
+# print("--- %s seconds ---" % (time.time() - exe_start_time))
+
+# import matplotlib.pyplot as plt
+
+# fig = plt.figure()
+# ax1 = fig.add_subplot(111, ylabel='Google price in $')
+# close_price.plot(ax=ax1, color='g', lw=2., legend=True)
+# sma.plot(ax=ax1, color='r', lw=2., legend=True)
+# plt.savefig('sma.png')
diff --git a/courses/sources/sec2/stdev.png b/courses/sources/sec2/stdev.png
new file mode 100644
index 0000000..1cda7fa
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diff --git a/courses/sources/sec2/stdev.py b/courses/sources/sec2/stdev.py
new file mode 100644
index 0000000..d870662
--- /dev/null
+++ b/courses/sources/sec2/stdev.py
@@ -0,0 +1,65 @@
+import os
+import time
+import statistics
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = 'goog_data.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+close = goog_data_signal['price']
+exe_start_time = time.time()
+
+import statistics as stats
+import math as math
+
+time_period = 20
+
+history = [] # history of prices
+sma_values = [] # to tracking sma
+stddev_values = [] # history of computed stdev values
+
+for close_price in close:
+    history.append(close_price)
+    if len(history) > time_period: # track at most 20 prices
+        del(history[0])
+
+    sma = stats.mean(history)
+    sma_values.append(sma)
+
+    variance = 0
+    for hist_price in history:
+        variance = variance + ((hist_price - sma) ** 2)
+    
+    stdev = math.sqrt(variance / len(history))
+    stddev_values.append(stdev)
+
+
+goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
+goog_data = goog_data.assign(StandardDeviationOver20Days=pd.Series(stddev_values, index=goog_data.index))
+
+""" Visualization """
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+ax1 = fig.add_subplot(211, ylabel='Google price in $')
+goog_data['ClosePrice'].plot(ax=ax1, color='g', lw=2., legend=True)
+ax2 = fig.add_subplot(212, ylabel='Stddev in $')
+goog_data['StandardDeviationOver20Days'].plot(ax=ax2, color='b', lw=2., legend=True)
+plt.savefig(dir_path + '/stdev.png')
+plt.show()
\ No newline at end of file
diff --git a/courses/sources/sec2/supportresistanceline.png b/courses/sources/sec2/supportresistanceline.png
new file mode 100644
index 0000000..79b3899
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diff --git a/courses/sources/sec2/supportresistanceline.py b/courses/sources/sec2/supportresistanceline.py
new file mode 100644
index 0000000..9a4890c
--- /dev/null
+++ b/courses/sources/sec2/supportresistanceline.py
@@ -0,0 +1,29 @@
+import pandas as pd
+from pandas_datareader import data
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = 'goog_data.pkl'
+
+try:
+    goog_data2 = pd.read_pickle(SRC_DATA_FILENAME)
+except FileNotFoundError:
+    goog_data2 = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data2.to_pickle(SRC_DATA_FILENAME)
+
+goog_data = goog_data2.tail(620)
+lows = goog_data['Low']
+highs = goog_data['High']
+print(goog_data)
+
+import matplotlib.pyplot as plt
+fig = plt.figure()
+ax1 = fig.add_subplot(111, ylabel='Google price in $')
+highs.plot(ax=ax1, color='c', lw=2.)
+lows.plot(ax=ax1, color='y', lw=2.)
+
+# Return the first 200 rows using pandas.DataFrame.head(200)
+plt.hlines(highs.head(200).max(), lows.index.values[0], lows.index.values[-1], linewidth=2, color='g') 
+plt.hlines(lows.head(200).min(), lows.index.values[0], lows.index.values[-1], linewidth=2, color='r')
+plt.axvline(linewidth=2, color='b', x=lows.index.values[200], linestyle=':')
+plt.savefig("supportresistanceline.png")
\ No newline at end of file
diff --git a/courses/sources/sec2/supportresistanceline_improved.png b/courses/sources/sec2/supportresistanceline_improved.png
new file mode 100644
index 0000000..d9c6fb5
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diff --git a/courses/sources/sec2/supportresistanceline_improved.py b/courses/sources/sec2/supportresistanceline_improved.py
new file mode 100644
index 0000000..cb562a8
--- /dev/null
+++ b/courses/sources/sec2/supportresistanceline_improved.py
@@ -0,0 +1,94 @@
+import pandas as pd
+import numpy as np
+from pandas_datareader import data
+
+start_date = '2014-01-01'
+end_date = '2018-01-01'
+SRC_DATA_FILENAME = 'goog_data.pkl'
+
+try:
+    goog_data = pd.read_pickle(SRC_DATA_FILENAME)
+    print('File data found...reading GOOG data')
+except FileNotFoundError:
+    print('File not found...downloading the GOOG data')
+    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
+    goog_data.to_pickle(SRC_DATA_FILENAME)
+
+goog_data_signal = pd.DataFrame(index=goog_data.index)
+goog_data_signal['price'] = goog_data['Adj Close']
+
+
+import numpy as np
+def trading_support_resistance(data, bin_width=20):
+
+    data['sup_tolerance'] = pd.Series(np.zeros(len(data))) # tolerance of support line
+    data['res_tolerance'] = pd.Series(np.zeros(len(data))) # tolerance of resistance line
+
+    data['sup_count'] = pd.Series(np.zeros(len(data))) # number of hitting support line
+    data['res_count'] = pd.Series(np.zeros(len(data)))
+
+    data['sup'] = pd.Series(np.zeros(len(data))) # support line value (constant)
+    data['res'] = pd.Series(np.zeros(len(data)))
+
+    data['position'] = pd.Series(np.zeros(len(data)))
+    data['signal'] = pd.Series(np.zeros(len(data)))
+
+    in_support = 0
+    in_resistance = 0
+
+    for x in range((bin_width-1) + bin_width, len(data)):
+        data_section = data[x-bin_width : x+1] # get data within rolling window
+        
+        support_level = min(data_section['price'])
+        resistance_level = max(data_section['price'])
+        range_level = resistance_level - support_level
+
+        data['res'][x] = resistance_level
+        data['sup'][x] = support_level
+
+        data['sup_tolerance'][x] = support_level + 0.2*range_level # 20% tolorance
+        data['res_tolerance'][x] = resistance_level - 0.2*range_level
+
+        if (data['price'][x] >= data['res_tolerance'][x]) and (data['price'][x] <= data['res'][x]):
+            # if price is within resistance tolerance region
+            in_resistance += 1
+            data['res_count'][x] = in_resistance
+        elif (data['price'][x] <= data['sup_tolerance'][x]) and (data['price'][x] >= data['sup'][x]):
+            # price is within support tolerance region
+            in_support += 1
+            data['sup_count'][x] = in_support
+        else:
+            # if not within any region, clear count
+            in_support = 0
+            in_resistance = 0
+
+        if in_resistance > 2:
+            # If enter resistance region twice
+            data['signal'][x] = 1
+        elif in_support > 2:
+            data['signal'][x] = 0
+        else:
+            data['signal'][x] = data['signal'][x-1]
+    
+    data['position'] = data['signal'].diff()
+
+trading_support_resistance(goog_data_signal)
+
+print(goog_data_signal)
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+ax1 = fig.add_subplot(111, ylabel='Google price in $')
+goog_data_signal['sup'].plot(ax=ax1, color='g', lw=2.)
+goog_data_signal['res'].plot(ax=ax1, color='b', lw=2.)
+goog_data_signal['price'].plot(ax=ax1, color='r', lw=2.)
+
+ax1.plot(goog_data_signal.loc[goog_data_signal.position == 1.0].index,
+        goog_data_signal.price[goog_data_signal.position == 1.0], '^', markersize=7, color='k', label='buy')
+ax1.plot(goog_data_signal.loc[goog_data_signal.position  == -1.0].index,
+        goog_data_signal.price[goog_data_signal.position == -1.0], 'v', markersize=7, color='k', label='sell')
+
+plt.legend()
+plt.savefig("supportresistanceline_improved")
+plt.show()
\ No newline at end of file
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