QLearningTrading-GoogleWalmart.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Deep Q- learning Agent- Stock Trading","metadata":{"id":"IGjkbK0lSeCi"}},{"cell_type":"markdown","source":"In this notebook, we will train an agent to trade 2 and 4 specific stocks at a time in order to maximize the profit. We will be using Deep Q-Learning.","metadata":{}},{"cell_type":"markdown","source":"## Getting the data from the Repo","metadata":{}},{"cell_type":"code","source":"!git clone https://github.com/Costopoulos/Stock-Trading-Reinforcement-Learning.git","metadata":{"id":"x5QKxSS4SqDF","outputId":"102e15a9-1e8d-4636-c8bb-3a6de76783c1","execution":{"iopub.status.busy":"2022-03-16T17:35:30.935099Z","iopub.execute_input":"2022-03-16T17:35:30.935449Z","iopub.status.idle":"2022-03-16T17:35:33.267212Z","shell.execute_reply.started":"2022-03-16T17:35:30.935353Z","shell.execute_reply":"2022-03-16T17:35:33.265810Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Cloning into 'Stock-Trading-Reinforcement-Learning'...\nremote: Enumerating objects: 39, done.\u001b[K\nremote: Counting objects: 100% (39/39), done.\u001b[K\nremote: Compressing objects: 100% (38/38), done.\u001b[K\nremote: Total 39 (delta 11), reused 0 (delta 0), pack-reused 0\u001b[K\nUnpacking objects: 100% (39/39), 1.10 MiB | 2.71 MiB/s, done.\n","output_type":"stream"}]},{"cell_type":"code","source":"%cd Stock-Trading-Reinforcement-Learning/","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:33.270544Z","iopub.execute_input":"2022-03-16T17:35:33.271295Z","iopub.status.idle":"2022-03-16T17:35:33.280585Z","shell.execute_reply.started":"2022-03-16T17:35:33.271239Z","shell.execute_reply":"2022-03-16T17:35:33.279431Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"/kaggle/working/Stock-Trading-Reinforcement-Learning\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Imports","metadata":{}},{"cell_type":"code","source":"import keras\nimport random\nimport math\nimport gym\nimport sys\nimport numpy as np\nimport pandas as pd\n\nfrom keras.models import Sequential\nfrom keras.models import load_model\nfrom keras.layers import Dense\nfrom tensorflow.keras.optimizers import Adam\n\nfrom datetime import datetime, timedelta\nfrom collections import deque","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:33.282753Z","iopub.execute_input":"2022-03-16T17:35:33.283355Z","iopub.status.idle":"2022-03-16T17:35:40.111695Z","shell.execute_reply.started":"2022-03-16T17:35:33.283309Z","shell.execute_reply":"2022-03-16T17:35:40.110529Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Important Definitions\n<ul> \n    <li><strong>stock1_name</strong>:   This is first stock name, is Apple - aapl.us</li>\n    <li><strong>stock2_name</strong>:   This is second stock name, is Amazon - amzn.us</li>\n    <li><strong>episode_count</strong>: This is number of episodes the agent will train on. In our case it's 50</li>\n    <li><strong>start_balance</strong>: This is the initial budget, which is $10,000</li>\n    <li><strong>training</strong>:      How many days each episode will last. Defaults at 1500</li>\n    <li><strong>test</strong>:          Number of days that the test will run on. Defaults at 500 </li>\n </ul>","metadata":{"id":"PvHknl9nSeCn"}},{"cell_type":"code","source":"# prints formatted price\ndef formatPrice(n):\n    return (\"-$\" if n < 0 else \"$\") + \"{0:.2f}\".format(abs(n))","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:40.113408Z","iopub.execute_input":"2022-03-16T17:35:40.114463Z","iopub.status.idle":"2022-03-16T17:35:40.120557Z","shell.execute_reply.started":"2022-03-16T17:35:40.114429Z","shell.execute_reply":"2022-03-16T17:35:40.119600Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"class Agent:\n    def __init__(self, state_size, is_eval=False, model_name=\"\"):\n        self.state_size = state_size # normalized previous days\n        self.action_size = 5 # buy_1, sell_1, do Nothing, buy2, sell2\n        self.memory = deque(maxlen=2000) # deque is basically a list that provides O(1) pop & append methods\n        self.inventory1 = []\n        self.inventory2 = []\n        self.model_name = model_name\n        self.is_eval = is_eval\n        self.gamma = 0.95 # gamma is the discount factor. It quantifies how much importance we give for future rewards.\n        self.epsilon = 1.0 # Exploration and Exploitation — Epsilon (ε) for ε - greedy\n        self.epsilon_min = 0.01\n        self.epsilon_decay = 0.995\n        self.model = load_model(\"models/\" + model_name) if is_eval else self._model()\n\n    def _model(self):\n        model = Sequential()\n        model.add(Dense(units=64, input_dim=self.state_size, activation=\"relu\"))\n        model.add(Dense(units=32, activation=\"relu\"))\n        model.add(Dense(units=8, activation=\"relu\"))\n        model.add(Dense(self.action_size, activation=\"linear\"))\n        model.compile(loss=\"mse\", optimizer=Adam(learning_rate=0.0001))\n        return model\n\n    def act(self, state):\n        if not self.is_eval and random.random() <= self.epsilon: # ε - greedy; act randomly if random returns sth < epsilon\n            return random.randrange(self.action_size)\n        options = self.model.predict(state)\n        return np.argmax(options[0])\n\n    def expReplay(self, batch_size):\n        mini_batch = []\n        l = len(self.memory)\n        \n        minibatch = random.sample(self.memory, batch_size) # minibatch\n        count = 0\n    \n        for state, action, reward, next_state, done in mini_batch:            \n            target = reward\n            if not done:\n                target = reward + self.gamma * np.amax(self.model.predict(next_state)[0])\n              \n            target_f = self.model.predict(state)\n            target_f[0][action] = target\n            self.model.fit(state, target_f, epochs=1, verbose=0)\n            count+=1\n\n        if self.epsilon > self.epsilon_min:\n            self.epsilon *= self.epsilon_decay ","metadata":{"id":"ljyQcIlBS3oE","execution":{"iopub.status.busy":"2022-03-16T17:35:40.128727Z","iopub.execute_input":"2022-03-16T17:35:40.129188Z","iopub.status.idle":"2022-03-16T17:35:40.148242Z","shell.execute_reply.started":"2022-03-16T17:35:40.129141Z","shell.execute_reply":"2022-03-16T17:35:40.147032Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"class State:\n    def __init__(self, data1, data2, Bal_stock1, Bal_stock2, open_cash, timestep):\n        self.Stock1Price=data1[timestep] # stock 1 open price\n        self.Stock2Price=data2[timestep] # stock 2 open price\n        self.Stock1Blnc=Bal_stock1 # stock 1 balance\n        self.Stock2Blnc=Bal_stock2 # stock 2 balance\n        self.open_cash=open_cash # cash balance\n        self.fiveday_stock1=self.five_day_window(data1, timestep)\n        self.fiveday_stock2=self.five_day_window(data2, timestep)\n        self.portfolio_value=self.portfolio_value()\n\n    def portfolio_value(self):\n        pvalue=0\n\n        v1=self.Stock1Price * float(self.Stock1Blnc)\n        v2=self.Stock2Price * float(self.Stock2Blnc)\n        v3=float(self.open_cash)\n        return (v1+v2+v3)\n    \n    def five_day_window(self,data, timestep):\n        step = timestep\n        if step < 5:\n            return data[0]\n        \n        stock_5days = np.mean(data[step-5:step])\n\n        return stock_5days\n    \n    def reset(self):\n        self.Stock1Price=151.25 # stock 1 open price Google\n        self.Stock2Price=21.845 # stock 2 open price Walmart\n        self.Stock1Blnc=34 # stock 1 balance Google\n        self.Stock2Blnc=221 # stock 2 balance Walmart\n        self.open_cash=10000 # cash balance\n        self.fiveday_stock1=151.25\n        self.fiveday_stock2=21.845\n        self.portfolio_value=10000\n        \n    def getState(self):\n        res=[]\n        res.append(self.Stock1Price) # stock 1 open price\n        res.append(self.Stock2Price) # stock 2 open price\n        res.append(self.Stock1Blnc) # stock 1 balance\n        res.append(self.Stock2Blnc) # stock 2 balance\n        res.append(self.open_cash) # cash balance\n        res.append(self.fiveday_stock1)\n        res.append(self.fiveday_stock2)        \n        res.append(self.portfolio_value)\n\n        res1=np.array([res])\n        return res1","metadata":{"id":"Hua1uaGLTBKN","execution":{"iopub.status.busy":"2022-03-16T17:35:40.150453Z","iopub.execute_input":"2022-03-16T17:35:40.150834Z","iopub.status.idle":"2022-03-16T17:35:40.169166Z","shell.execute_reply.started":"2022-03-16T17:35:40.150789Z","shell.execute_reply":"2022-03-16T17:35:40.167989Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"stock_name1, stock_name2, episode_count, start_balance, training, test = 'data/googl.us.txt', 'data/wm.us.txt', 12, 50000, 2500, 500 #1500, 500\n\npd_data1 = pd.read_csv(stock_name1, sep=\",\", header=0)\npd_data2 = pd.read_csv(stock_name2, sep=\",\", header=0)","metadata":{"id":"L6n9GMfpSeCo","execution":{"iopub.status.busy":"2022-03-16T17:35:40.171108Z","iopub.execute_input":"2022-03-16T17:35:40.171567Z","iopub.status.idle":"2022-03-16T17:35:40.198655Z","shell.execute_reply.started":"2022-03-16T17:35:40.171503Z","shell.execute_reply":"2022-03-16T17:35:40.197712Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"## Data Pre-processing","metadata":{"id":"U9wHNHx0SeCq"}},{"cell_type":"markdown","source":"In this section we will do the following:\n1. Look at the data of Apple and Amazon stock for checking anamolies (missing data etc.). Also convert date into right format\n2. Make sure that both the stock data is for the same time period and same days in this time period. Remove data if necessary\n3. Look at descriptive statistics of data- mean, median, number of records\n4. Visualize the data to see how stock price changes with time\n    ","metadata":{"id":"ixCMRL2ESeCq"}},{"cell_type":"markdown","source":"#### View First Stock's data","metadata":{"id":"I6FUGekoSeCr"}},{"cell_type":"code","source":"pd_data1.head()","metadata":{"id":"Xb9f9nKPSeCs","outputId":"ecb4afee-ed89-4aaf-d855-067bcb2a7e5e","execution":{"iopub.status.busy":"2022-03-16T17:35:40.200414Z","iopub.execute_input":"2022-03-16T17:35:40.200878Z","iopub.status.idle":"2022-03-16T17:35:40.227209Z","shell.execute_reply.started":"2022-03-16T17:35:40.200810Z","shell.execute_reply":"2022-03-16T17:35:40.226170Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"         Date    Open    High     Low   Close    Volume  OpenInt\n0  2005-06-29  151.25  152.19  146.07  146.36  36597400        0\n1  2005-06-30  147.17  149.46  145.52  147.07  30188800        0\n2  2005-07-01  147.52  148.12  144.61  145.62  18455200        0\n3  2005-07-05  146.05  147.99  145.12  147.85  14988000        0\n4  2005-07-06  148.65  148.80  145.69  145.76  16000600        0","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>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>OpenInt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2005-06-29</td>\n      <td>151.25</td>\n      <td>152.19</td>\n      <td>146.07</td>\n      <td>146.36</td>\n      <td>36597400</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2005-06-30</td>\n      <td>147.17</td>\n      <td>149.46</td>\n      <td>145.52</td>\n      <td>147.07</td>\n      <td>30188800</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2005-07-01</td>\n      <td>147.52</td>\n      <td>148.12</td>\n      <td>144.61</td>\n      <td>145.62</td>\n      <td>18455200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2005-07-05</td>\n      <td>146.05</td>\n      <td>147.99</td>\n      <td>145.12</td>\n      <td>147.85</td>\n      <td>14988000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2005-07-06</td>\n      <td>148.65</td>\n      <td>148.80</td>\n      <td>145.69</td>\n      <td>145.76</td>\n      <td>16000600</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"pd_data1.describe()","metadata":{"id":"oCj4Abn8SeCz","outputId":"cda36ec0-ba21-4f47-fba7-093d9f352466","execution":{"iopub.status.busy":"2022-03-16T17:35:40.229220Z","iopub.execute_input":"2022-03-16T17:35:40.229628Z","iopub.status.idle":"2022-03-16T17:35:40.278213Z","shell.execute_reply.started":"2022-03-16T17:35:40.229582Z","shell.execute_reply":"2022-03-16T17:35:40.277216Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"              Open         High          Low        Close        Volume  \\\ncount  3116.000000  3116.000000  3116.000000  3116.000000  3.116000e+03   \nmean    410.619523   414.130549   406.671010   410.463003  6.970182e+06   \nstd     229.235665   230.422523   227.837434   229.258823  6.852298e+06   \nmin     131.260000   134.680000   123.650000   128.720000  0.000000e+00   \n25%     240.110000   242.365000   237.560000   239.555000  2.378724e+06   \n50%     304.130000   306.795000   301.340000   304.175000  4.828672e+06   \n75%     559.432500   562.535000   554.155000   558.480000  8.971350e+06   \nmax    1050.050000  1063.620000  1047.050000  1058.290000  8.223340e+07   \n\n       OpenInt  \ncount   3116.0  \nmean       0.0  \nstd        0.0  \nmin        0.0  \n25%        0.0  \n50%        0.0  \n75%        0.0  \nmax        0.0  ","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>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>OpenInt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>3116.000000</td>\n      <td>3116.000000</td>\n      <td>3116.000000</td>\n      <td>3116.000000</td>\n      <td>3.116000e+03</td>\n      <td>3116.0</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>410.619523</td>\n      <td>414.130549</td>\n      <td>406.671010</td>\n      <td>410.463003</td>\n      <td>6.970182e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>229.235665</td>\n      <td>230.422523</td>\n      <td>227.837434</td>\n      <td>229.258823</td>\n      <td>6.852298e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>131.260000</td>\n      <td>134.680000</td>\n      <td>123.650000</td>\n      <td>128.720000</td>\n      <td>0.000000e+00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>240.110000</td>\n      <td>242.365000</td>\n      <td>237.560000</td>\n      <td>239.555000</td>\n      <td>2.378724e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>304.130000</td>\n      <td>306.795000</td>\n      <td>301.340000</td>\n      <td>304.175000</td>\n      <td>4.828672e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>559.432500</td>\n      <td>562.535000</td>\n      <td>554.155000</td>\n      <td>558.480000</td>\n      <td>8.971350e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1050.050000</td>\n      <td>1063.620000</td>\n      <td>1047.050000</td>\n      <td>1058.290000</td>\n      <td>8.223340e+07</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"#### View Second Stock's data\n","metadata":{"id":"Uav-jthtSeCt"}},{"cell_type":"code","source":"pd_data2.head()","metadata":{"id":"OwzFUAZWSeCv","outputId":"0ef0dfd6-eb36-4151-9b82-83c3ca3eb10c","execution":{"iopub.status.busy":"2022-03-16T17:35:40.280083Z","iopub.execute_input":"2022-03-16T17:35:40.280428Z","iopub.status.idle":"2022-03-16T17:35:40.295693Z","shell.execute_reply.started":"2022-03-16T17:35:40.280384Z","shell.execute_reply":"2022-03-16T17:35:40.294574Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"         Date    Open    High     Low   Close   Volume  OpenInt\n0  2005-02-25  23.122  23.495  22.964  23.434  1799314        0\n1  2005-02-28  23.384  23.448  23.067  23.177  2267562        0\n2  2005-03-01  23.202  23.607  23.202  23.535  2072350        0\n3  2005-03-02  23.425  23.670  23.306  23.607  1777398        0\n4  2005-03-03  23.590  23.639  23.344  23.400  1584080        0","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>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>OpenInt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2005-02-25</td>\n      <td>23.122</td>\n      <td>23.495</td>\n      <td>22.964</td>\n      <td>23.434</td>\n      <td>1799314</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2005-02-28</td>\n      <td>23.384</td>\n      <td>23.448</td>\n      <td>23.067</td>\n      <td>23.177</td>\n      <td>2267562</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2005-03-01</td>\n      <td>23.202</td>\n      <td>23.607</td>\n      <td>23.202</td>\n      <td>23.535</td>\n      <td>2072350</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2005-03-02</td>\n      <td>23.425</td>\n      <td>23.670</td>\n      <td>23.306</td>\n      <td>23.607</td>\n      <td>1777398</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2005-03-03</td>\n      <td>23.590</td>\n      <td>23.639</td>\n      <td>23.344</td>\n      <td>23.400</td>\n      <td>1584080</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"pd_data2.describe()","metadata":{"id":"1iJ2bc-LSeC0","outputId":"6c7d6998-a25d-4d60-ca9b-65d84d388d3f","execution":{"iopub.status.busy":"2022-03-16T17:35:40.297432Z","iopub.execute_input":"2022-03-16T17:35:40.298295Z","iopub.status.idle":"2022-03-16T17:35:40.337185Z","shell.execute_reply.started":"2022-03-16T17:35:40.298241Z","shell.execute_reply":"2022-03-16T17:35:40.336065Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"             Open         High          Low        Close        Volume  \\\ncount  3201.00000  3201.000000  3201.000000  3201.000000  3.201000e+03   \nmean     36.06852    36.340342    35.798395    36.077707  3.222051e+06   \nstd      14.71595    14.755393    14.708306    14.734227  2.021389e+06   \nmin      17.78500    18.317000    17.548000    17.652000  5.565350e+05   \n25%      26.67900    26.924000    26.399000    26.656000  1.936468e+06   \n50%      29.34200    29.552000    29.094000    29.326000  2.678160e+06   \n75%      42.82800    43.159000    42.601000    42.828000  3.838658e+06   \nmax      82.31000    82.870000    81.700000    82.310000  2.253831e+07   \n\n       OpenInt  \ncount   3201.0  \nmean       0.0  \nstd        0.0  \nmin        0.0  \n25%        0.0  \n50%        0.0  \n75%        0.0  \nmax        0.0  ","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>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>OpenInt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>3201.00000</td>\n      <td>3201.000000</td>\n      <td>3201.000000</td>\n      <td>3201.000000</td>\n      <td>3.201000e+03</td>\n      <td>3201.0</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>36.06852</td>\n      <td>36.340342</td>\n      <td>35.798395</td>\n      <td>36.077707</td>\n      <td>3.222051e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>14.71595</td>\n      <td>14.755393</td>\n      <td>14.708306</td>\n      <td>14.734227</td>\n      <td>2.021389e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>17.78500</td>\n      <td>18.317000</td>\n      <td>17.548000</td>\n      <td>17.652000</td>\n      <td>5.565350e+05</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>26.67900</td>\n      <td>26.924000</td>\n      <td>26.399000</td>\n      <td>26.656000</td>\n      <td>1.936468e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>29.34200</td>\n      <td>29.552000</td>\n      <td>29.094000</td>\n      <td>29.326000</td>\n      <td>2.678160e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>42.82800</td>\n      <td>43.159000</td>\n      <td>42.601000</td>\n      <td>42.828000</td>\n      <td>3.838658e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>82.31000</td>\n      <td>82.870000</td>\n      <td>81.700000</td>\n      <td>82.310000</td>\n      <td>2.253831e+07</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Drop Data that is not in both stock data- some days' data is missing in stock1 and some in stock2","metadata":{"id":"wwKF8H_USeC1"}},{"cell_type":"code","source":"# Convert Date to DateTime\ndef dateToDatetime(df1, df2):\n    df1['Date'] = pd.to_datetime(df1['Date'], format='%Y/%m/%d')\n    df2['Date'] = pd.to_datetime(df2['Date'], format='%Y/%m/%d')\n    return df1, df2\n\ndef firstFilteringOut(df1, df2):\n    if (df1['Date'][0] > df2['Date'][0]): \n        df1 = df1[df1.Date>=df2['Date'][0]]\n        df1 = df1.reset_index(drop=True)\n    else:\n        df2 = df2[df2.Date>=df1['Date'][0]]\n        df2 = df2.reset_index(drop=True)\n    \n    return df1, df2\n\n# Drop columns for which data is missing in stock1 or stock2\ndef dropDataNotInBothStocks(df1, df2):\n    df1, df2 = firstFilteringOut(df1, df2)\n    list1 = df1['Date']\n    list2 = df2['Date']\n    diff_df1 = list(set(list1) - set(list2))\n    diff_df2 = list(set(list2) - set(list1))\n    \n    for k in range(len(diff_df1)):\n        df1_datetime_str = diff_df1[k].strftime('%Y-%m-%d 00:00:00')\n        df1_datetime = datetime.strptime(df1_datetime_str, \"%Y-%m-%d 00:00:00\")\n        for i, j in enumerate(list1):\n            if j == df1_datetime:\n                df1 = df1.drop([i])\n    \n    for k in range(len(diff_df2)):\n        df2_datetime_str = diff_df2[k].strftime('%Y-%m-%d 00:00:00')\n        df2_datetime = datetime.strptime(df2_datetime_str, \"%Y-%m-%d 00:00:00\")\n        for i, j in enumerate(list2):\n            if j == df2_datetime:\n                df2 = df2.drop([i])\n    \n    df1 = df1.reset_index(drop=True)\n    df2 = df2.reset_index(drop=True)\n    \n    return df1, df2\n\ndef preprocess2Stocks(df1, df2):\n    df1, df2 = dateToDatetime(df1, df2)\n    df1, df2 = dropDataNotInBothStocks(df1, df2)\n    return df1, df2","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:40.339290Z","iopub.execute_input":"2022-03-16T17:35:40.339826Z","iopub.status.idle":"2022-03-16T17:35:40.355427Z","shell.execute_reply.started":"2022-03-16T17:35:40.339766Z","shell.execute_reply":"2022-03-16T17:35:40.354052Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"pd_data1, pd_data2 = preprocess2Stocks(pd_data1, pd_data2)","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:40.357521Z","iopub.execute_input":"2022-03-16T17:35:40.358254Z","iopub.status.idle":"2022-03-16T17:35:41.049692Z","shell.execute_reply.started":"2022-03-16T17:35:40.358205Z","shell.execute_reply":"2022-03-16T17:35:41.048606Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"Now the data is clean, both stock1 and stock2 have 5151 records","metadata":{"id":"F5enSyBbSeC6"}},{"cell_type":"code","source":"pd_data1['Date'][0]","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:41.054469Z","iopub.execute_input":"2022-03-16T17:35:41.054747Z","iopub.status.idle":"2022-03-16T17:35:41.064702Z","shell.execute_reply.started":"2022-03-16T17:35:41.054716Z","shell.execute_reply":"2022-03-16T17:35:41.063340Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"Timestamp('2005-06-29 00:00:00')"},"metadata":{}}]},{"cell_type":"code","source":"pd_data2['Date'][0]","metadata":{"id":"l4qnuWloSeC3","outputId":"b8d25f68-a2da-48b4-f535-4558691e9df7","execution":{"iopub.status.busy":"2022-03-16T17:35:41.066484Z","iopub.execute_input":"2022-03-16T17:35:41.066952Z","iopub.status.idle":"2022-03-16T17:35:41.078488Z","shell.execute_reply.started":"2022-03-16T17:35:41.066875Z","shell.execute_reply":"2022-03-16T17:35:41.077304Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"Timestamp('2005-06-29 00:00:00')"},"metadata":{}}]},{"cell_type":"code","source":"pd_data1.describe()","metadata":{"id":"OrrvIcIaSeC6","outputId":"d1c7d736-723e-4ce9-81b1-0c39c6730e5b","execution":{"iopub.status.busy":"2022-03-16T17:35:41.080025Z","iopub.execute_input":"2022-03-16T17:35:41.080778Z","iopub.status.idle":"2022-03-16T17:35:41.116731Z","shell.execute_reply.started":"2022-03-16T17:35:41.080731Z","shell.execute_reply":"2022-03-16T17:35:41.115627Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"              Open         High          Low        Close        Volume  \\\ncount  3115.000000  3115.000000  3115.000000  3115.000000  3.115000e+03   \nmean    410.651658   414.162816   406.701999   410.494410  6.971470e+06   \nstd     229.265450   230.452477   227.867446   229.288926  6.853021e+06   \nmin     131.260000   134.680000   123.650000   128.720000  0.000000e+00   \n25%     240.100000   242.350000   237.560000   239.550000  2.376398e+06   \n50%     304.100000   306.690000   301.280000   304.170000  4.829344e+06   \n75%     559.455000   562.570000   554.160000   558.510000  8.972900e+06   \nmax    1050.050000  1063.620000  1047.050000  1058.290000  8.223340e+07   \n\n       OpenInt  \ncount   3115.0  \nmean       0.0  \nstd        0.0  \nmin        0.0  \n25%        0.0  \n50%        0.0  \n75%        0.0  \nmax        0.0  ","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>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>OpenInt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>3115.000000</td>\n      <td>3115.000000</td>\n      <td>3115.000000</td>\n      <td>3115.000000</td>\n      <td>3.115000e+03</td>\n      <td>3115.0</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>410.651658</td>\n      <td>414.162816</td>\n      <td>406.701999</td>\n      <td>410.494410</td>\n      <td>6.971470e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>229.265450</td>\n      <td>230.452477</td>\n      <td>227.867446</td>\n      <td>229.288926</td>\n      <td>6.853021e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>131.260000</td>\n      <td>134.680000</td>\n      <td>123.650000</td>\n      <td>128.720000</td>\n      <td>0.000000e+00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>240.100000</td>\n      <td>242.350000</td>\n      <td>237.560000</td>\n      <td>239.550000</td>\n      <td>2.376398e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>304.100000</td>\n      <td>306.690000</td>\n      <td>301.280000</td>\n      <td>304.170000</td>\n      <td>4.829344e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>559.455000</td>\n      <td>562.570000</td>\n      <td>554.160000</td>\n      <td>558.510000</td>\n      <td>8.972900e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1050.050000</td>\n      <td>1063.620000</td>\n      <td>1047.050000</td>\n      <td>1058.290000</td>\n      <td>8.223340e+07</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"pd_data2.describe()","metadata":{"id":"dvIT5iCnSeC6","outputId":"2b15c8f9-311b-4b6e-b523-7ecf6bbbc7f2","execution":{"iopub.status.busy":"2022-03-16T17:35:41.118320Z","iopub.execute_input":"2022-03-16T17:35:41.119380Z","iopub.status.idle":"2022-03-16T17:35:41.154680Z","shell.execute_reply.started":"2022-03-16T17:35:41.119301Z","shell.execute_reply":"2022-03-16T17:35:41.153628Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"              Open         High          Low        Close        Volume  \\\ncount  3115.000000  3115.000000  3115.000000  3115.000000  3.115000e+03   \nmean     36.427925    36.702963    36.155391    36.437733  3.254244e+06   \nstd      14.755491    14.793010    14.749815    14.773647  2.037093e+06   \nmin      17.785000    18.317000    17.548000    17.652000  5.565350e+05   \n25%      26.879500    27.118000    26.607000    26.880000  1.963397e+06   \n50%      29.493000    29.675000    29.246000    29.489000  2.719352e+06   \n75%      43.347000    43.571000    43.086000    43.375000  3.891154e+06   \nmax      82.310000    82.870000    81.700000    82.310000  2.253831e+07   \n\n       OpenInt  \ncount   3115.0  \nmean       0.0  \nstd        0.0  \nmin        0.0  \n25%        0.0  \n50%        0.0  \n75%        0.0  \nmax        0.0  ","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>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>OpenInt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>3115.000000</td>\n      <td>3115.000000</td>\n      <td>3115.000000</td>\n      <td>3115.000000</td>\n      <td>3.115000e+03</td>\n      <td>3115.0</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>36.427925</td>\n      <td>36.702963</td>\n      <td>36.155391</td>\n      <td>36.437733</td>\n      <td>3.254244e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>14.755491</td>\n      <td>14.793010</td>\n      <td>14.749815</td>\n      <td>14.773647</td>\n      <td>2.037093e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>17.785000</td>\n      <td>18.317000</td>\n      <td>17.548000</td>\n      <td>17.652000</td>\n      <td>5.565350e+05</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>26.879500</td>\n      <td>27.118000</td>\n      <td>26.607000</td>\n      <td>26.880000</td>\n      <td>1.963397e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>29.493000</td>\n      <td>29.675000</td>\n      <td>29.246000</td>\n      <td>29.489000</td>\n      <td>2.719352e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>43.347000</td>\n      <td>43.571000</td>\n      <td>43.086000</td>\n      <td>43.375000</td>\n      <td>3.891154e+06</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>82.310000</td>\n      <td>82.870000</td>\n      <td>81.700000</td>\n      <td>82.310000</td>\n      <td>2.253831e+07</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### Data Visualization","metadata":{"id":"PVzm5Tv7SeC7"}},{"cell_type":"code","source":"# Stock1 Plot\nimport matplotlib.pyplot as plt\nimport datetime\nimport numpy as np\n\n%matplotlib inline\n\nx1 = np.array(pd_data1['Date'])\ny1 = pd_data1['Open']\ny12= pd_data1['Volume']\n\nplt.title(\"Apple Stock Performance Over years\")\nplt.xlabel(\"Year\")\nplt.ylabel(\"Price in $\")\nplt.plot(x1,y1)\n\n\nax2 = plt.twinx()  # instantiate a second axes that shares the same x-axis\n\ncolor = 'tab:red'\nax2.set_ylabel('volume', color=color)  # we already handled the x-label with ax1\nax2.plot(x1, y12, color=color)\nax2.tick_params(axis='y', labelcolor=color)\n\n\nplt.show()","metadata":{"id":"srEV4LBzSeC7","outputId":"c695c3fc-3300-4255-fcae-685dd274d8c1","execution":{"iopub.status.busy":"2022-03-16T17:35:41.156023Z","iopub.execute_input":"2022-03-16T17:35:41.156870Z","iopub.status.idle":"2022-03-16T17:35:41.540072Z","shell.execute_reply.started":"2022-03-16T17:35:41.156824Z","shell.execute_reply":"2022-03-16T17:35:41.539107Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Stock2 Plot\nimport matplotlib.pyplot as plt\nimport datetime\nimport numpy as np\n\n%matplotlib inline\n\nx2 = np.array(pd_data2['Date'])\ny2 = pd_data2['Open']\ny22= pd_data2['Volume']\n\n\nplt.title(\"Amazon Stock Performance Over years\")\nplt.xlabel(\"Year\")\nplt.ylabel(\"Price in $\")\n\nplt.plot(x2,y2)\n\n\nax2 = plt.twinx()  # instantiate a second axes that shares the same x-axis\n\ncolor = 'tab:red'\nax2.set_ylabel('volume', color=color)  # we already handled the x-label with ax1\nax2.plot(x2, y22, color=color)\nax2.tick_params(axis='y', labelcolor=color)\n\n\nplt.show()","metadata":{"id":"vAG8KwtiSeC8","outputId":"cd92ac62-d513-4701-8091-7311732facd3","execution":{"iopub.status.busy":"2022-03-16T17:35:41.541892Z","iopub.execute_input":"2022-03-16T17:35:41.542470Z","iopub.status.idle":"2022-03-16T17:35:41.906867Z","shell.execute_reply.started":"2022-03-16T17:35:41.542417Z","shell.execute_reply":"2022-03-16T17:35:41.905853Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Split Training and Test data","metadata":{"id":"mUfoTNV1SeC8"}},{"cell_type":"markdown","source":"Next step is to divide data into training and test set. This has to be done in a chronological order. So, data is divided into:\n- training set from days 0 to days 'training'\n- test set: from days 'training' to day 'training+ test'","metadata":{"id":"xbkC4RalSeC8"}},{"cell_type":"code","source":"def train_test_split(df, train_size, test_size):\n    df_train = df[0:train_size]\n    df_test = df[train_size:train_size+test_size]\n    \n    return df_train, df_test","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:41.908597Z","iopub.execute_input":"2022-03-16T17:35:41.909159Z","iopub.status.idle":"2022-03-16T17:35:41.916984Z","shell.execute_reply.started":"2022-03-16T17:35:41.909112Z","shell.execute_reply":"2022-03-16T17:35:41.915817Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"# Split\npd_data1_train, pd_data1_test = train_test_split(pd_data1, training, test)\npd_data2_train, pd_data2_test = train_test_split(pd_data2, training, test)","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:41.920365Z","iopub.execute_input":"2022-03-16T17:35:41.921156Z","iopub.status.idle":"2022-03-16T17:35:41.928654Z","shell.execute_reply.started":"2022-03-16T17:35:41.921108Z","shell.execute_reply":"2022-03-16T17:35:41.927703Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"markdown","source":"Make the pd_data'INDEX'_test variables act like lists (have numerical indexes)","metadata":{}},{"cell_type":"code","source":"pd_data1_test = pd_data1_test.reset_index(drop=True)\npd_data2_test = pd_data2_test.reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:41.930002Z","iopub.execute_input":"2022-03-16T17:35:41.930929Z","iopub.status.idle":"2022-03-16T17:35:41.940072Z","shell.execute_reply.started":"2022-03-16T17:35:41.930795Z","shell.execute_reply":"2022-03-16T17:35:41.939015Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"## Initialize state and set benchmarking model","metadata":{}},{"cell_type":"code","source":"def benchmark(df1, df2, train_size, initial_balance, test=False):\n    \"\"\"\n    df1, df2: The stocks\n    train_size: The size (days) of the training\n    initial_balance: The starting wallet balance\n    \"\"\"\n    data1 = df1['Open']\n    data2 = df2['Open']\n    \n    data1Date = df1['Date']\n    \n    benchStock1Balance = int(np.floor((initial_balance/4)/data1[0]))\n    benchStock2Balance = int(np.floor((initial_balance/4)/data2[0]))\n    openCash = initial_balance/2\n    \n    # sell 10% of stock in 10 intervals\n    interval = int(train_size/10)\n    \n    stock1Balance = np.floor(benchStock1Balance/10)\n    stock2Balance = np.floor(benchStock2Balance/10)\n    \n    benchmarkValue = []\n    \n    for i in range(interval, train_size+1, interval):\n        closingStock1Price = data1[i-1]\n        closingStock2Price = data2[i-1]\n\n        stock1Date = data1Date[i-1].strftime('%Y-%m-%d')\n\n        stocks1Value = stock1Balance * closingStock1Price\n        stocks2Value = stock2Balance * closingStock2Price\n        \n        benchStock1Balance -= stock1Balance\n        benchStock2Balance -= stock2Balance\n\n        stock1PortValue = benchStock1Balance * closingStock1Price\n        stock2PortValue = benchStock2Balance * closingStock2Price\n        \n        openCash += stocks1Value + stocks2Value #Adding 10% sold value into open cash\n\n        totalPortfolioValue = openCash + stock1PortValue + stock2PortValue\n        benchmarkValue.append([stock1Date, benchStock1Balance, benchStock2Balance, totalPortfolioValue])\n    \n    return data1, data2, benchmarkValue","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:41.942513Z","iopub.execute_input":"2022-03-16T17:35:41.943692Z","iopub.status.idle":"2022-03-16T17:35:41.959559Z","shell.execute_reply.started":"2022-03-16T17:35:41.943643Z","shell.execute_reply":"2022-03-16T17:35:41.958576Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"data1_train, data2_train, benchValue = benchmark(pd_data1_train, pd_data2_train, training, start_balance)\nprint(f'Benchmark Profit is {benchValue[-1][3]} with Stock1: {benchValue[-1][1]} and Stock2: {benchValue[-1][2]}')","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:41.965737Z","iopub.execute_input":"2022-03-16T17:35:41.967727Z","iopub.status.idle":"2022-03-16T17:35:41.986192Z","shell.execute_reply.started":"2022-03-16T17:35:41.967675Z","shell.execute_reply":"2022-03-16T17:35:41.984952Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"Benchmark Profit is 70032.941 with Stock1: 2.0 and Stock2: 7.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Training","metadata":{"id":"O5uHfrvXSeDA"}},{"cell_type":"code","source":"def qLearningTrading(df1, df2, train_size, initial_balance, batch_size):\n    \"\"\"\n    df1, df2: The train or the test data of each stock\n    train_size: The size (days) of the training\n    initial_balance: The starting wallet balance\n    batch_size: The size used for the agent's experience replay method\n    \"\"\"\n    # Define arrays to store per episode values \n    total_Prof = []\n    total_stock1bal = []\n    total_stock2bal = []\n    total_open_cash = []\n    total_port_value = []\n    total_days_played = []\n    \n    for e in range(episode_count + 1):\n        print(\"..........\")\n        print(f\"Episode {e}/{episode_count}\")\n\n        benchStock1Balance = int(np.floor((initial_balance/4)/df1[0]))\n        benchStock2Balance = int(np.floor((initial_balance/4)/df2[0]))\n        openCash = initial_balance/2\n\n        done = False\n        total_profit = 0\n        reward = 0\n\n        # Initialize Agent\n        agent = Agent(8)\n        agent.inventory1 = []\n        agent.inventory2 = []\n        for i in range(benchStock1Balance):\n            agent.inventory1.append(df1[0])\n        for i in range(benchStock2Balance):\n            agent.inventory2.append(df2[0]) \n\n\n        # Running episode over all days in the train_size\n        for t in range(train_size):\n            stateClassObj = State(df1, df2, benchStock1Balance, benchStock2Balance, openCash, t)\n            stateArrayObj = stateClassObj.getState()\n            action = agent.act(stateArrayObj)\n\n            changePercentStock1 = (stateClassObj.Stock1Price - stateClassObj.fiveday_stock1) / stateClassObj.fiveday_stock1 * 100\n            changePercentStock2 = (stateClassObj.Stock2Price - stateClassObj.fiveday_stock2) / stateClassObj.fiveday_stock2 * 100\n\n            if action == 0:  # Buy stock 1\n                if stateClassObj.Stock1Price > stateClassObj.open_cash:\n    #                 print(\"Bought stock 1 when agent did not have money. File for bankruptcy and end of episode\")\n                    reward = -200000\n                    done = True # end of episode\n\n                else:\n                    agent.inventory1.append(df1[t])\n                    stock1Balance = len(agent.inventory1)\n                    stock2Balance = len(agent.inventory2)\n                    cash = stateClassObj.open_cash - stateClassObj.Stock1Price # Buy 1 stock\n\n                    if(stateClassObj.open_cash < 500):\n                        reward = -100000\n                    elif (abs(changePercentStock1) <= 2):\n                        reward = -10000\n                    else:  \n                        reward = -changePercentStock1 * 100\n\n            if action == 1:  # Sell stock 1\n                if stateClassObj.Stock1Blnc < 1:# or len(agent.inventory1) < 1:\n    #                 print(\"Sold stock 1 when agent did not have any stock1. File for bankruptcy and end of episode\")\n                    reward = -200000\n                    done = True # end of episode\n\n                else:\n                    boughtPrice1 = agent.inventory1.pop(0)\n                    stock1Balance = len(agent.inventory1)\n                    stock2Balance = len(agent.inventory2)\n                    cash = stateClassObj.open_cash + stateClassObj.Stock1Price # Sell 1 stock\n\n                    if (stateClassObj.Stock1Blnc < 10):\n                        reward = -100000\n                    elif (abs(changePercentStock1) <= 2):\n                        reward = -10000\n                    else:\n                        reward = changePercentStock1 * 100\n                        \n#                     total_profit += stateClassObj.Stock1Price - boughtPrice1\n                    \n            if action == 2: # Do nothing action    \n                if (abs(changePercentStock1) <= 2) and (abs(changePercentStock2) <= 2):\n                    reward = 10000\n                elif (stateClassObj.open_cash < 0.1 * initial_balance):\n                    reward = 1000000\n                elif (abs(changePercentStock1) <= 2) or (abs(changePercentStock2) <= 2):\n                    reward = 1000\n                else:\n                    reward = -100000\n\n                stock1Balance = len(agent.inventory1)\n                stock2Balance = len(agent.inventory2)\n                cash = openCash\n\n\n            if action == 3:  # Buy stock 2\n                if stateClassObj.Stock2Price > stateClassObj.open_cash:\n    #                 print(\"Bought stock 2 when agent did not have money. File for bankruptcy and end of episode\")\n                    reward = -200000\n                    done = True\n\n                else:\n                    agent.inventory2.append(df2[t])\n                    stock1Balance = len(agent.inventory1)\n                    stock2Balance = len(agent.inventory2)\n                    cash = stateClassObj.open_cash - stateClassObj.Stock2Price\n\n                    if (stateClassObj.open_cash < 5000):\n                        reward = -100000\n                    elif (abs(changePercentStock2) <= 2):\n                        reward = -10000\n                    else:\n                        reward = -changePercentStock2 * 100\n\n\n            if action == 4:  # Sell stock 2\n                if stateClassObj.Stock2Blnc < 1:# or len(agent.inventory2) < 1:\n    #                 print(\"Sold stock 2 when agent did not have any stock2. File for bankruptcy and end of episode\")\n                    reward = -200000\n                    done = True\n                else:\n                    boughtPrice2 = agent.inventory2.pop(0)\n                    stock1Balance = len(agent.inventory1)\n                    stock2Balance = len(agent.inventory2)\n                    cash = stateClassObj.open_cash + stateClassObj.Stock2Price # Sell 1 stock\n\n                    if (stateClassObj.Stock2Blnc < 10):\n                        reward = -100000\n                    elif (abs(changePercentStock2) <= 2):\n                        reward = -10000\n                    else:\n                        reward = changePercentStock2 * 100 \n\n#                     total_profit += stateClassObj.Stock2Price - boughtPrice2\n\n            if t == train_size - 1:\n                done = True\n                nextStateClassObj = State(df1, df2, stock1Balance, stock2Balance, cash, t)\n                nextStateArrayObj = nextStateClassObj.getState()\n            else:\n                nextStateClassObj = State(df1, df2, stock1Balance, stock2Balance, cash, t + 1)\n                nextStateArrayObj = nextStateClassObj.getState()\n\n            agent.memory.append((stateClassObj, action, reward, nextStateArrayObj, done))\n\n            benchStock1Balance = stock1Balance\n            benchStock2Balance = stock2Balance\n            openCash = cash\n\n            if done == True:\n                total_Prof.append(nextStateClassObj.portfolio_value - initial_balance)\n                total_stock1bal.append(len(agent.inventory1))\n                total_stock2bal.append(len(agent.inventory2))\n                total_open_cash.append(stateClassObj.open_cash)\n                total_port_value.append(stateClassObj.portfolio_value)\n                total_days_played.append(t)\n\n                print(\"--------------------------------\")\n                stateClassObj.reset()\n                break\n\n            if len(agent.memory) > batch_size:\n                agent.expReplay(batch_size)\n\n        if e % 10 == 0:\n            agent.model.save(\"models/model_ep\" + str(e))\n\n    return total_Prof, total_stock1bal, total_stock2bal, total_open_cash, total_port_value, total_days_played, agent","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:41.989661Z","iopub.execute_input":"2022-03-16T17:35:41.990343Z","iopub.status.idle":"2022-03-16T17:35:42.024385Z","shell.execute_reply.started":"2022-03-16T17:35:41.990294Z","shell.execute_reply":"2022-03-16T17:35:42.023163Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"total_Prof, total_stock1bal, total_stock2bal, total_open_cash, total_port_value, total_days_played, agent_train = qLearningTrading(data1_train, data2_train, training, start_balance, 64)\n\nprint(f\"Total Apple stocks in episodes: {total_stock1bal}\")\nprint(f\"Total Amazon stocks in episodes: {total_stock2bal}\")\nprint(f\"Total Open cash in episodes: {total_open_cash}\")\nprint(f\"Total Portfolio value in episodes: {total_port_value}\")\nprint(f\"Total Days in episodes: {total_days_played}\")\nprint(f\"Total Profit in episodes: {total_Prof}\")\n\nprint(f'Benchmark Profit is {benchValue[-1][3]} with Stock1: {benchValue[-1][1]} and Stock2: {benchValue[-1][2]}')","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:35:42.027903Z","iopub.execute_input":"2022-03-16T17:35:42.028465Z","iopub.status.idle":"2022-03-16T17:46:38.576315Z","shell.execute_reply.started":"2022-03-16T17:35:42.028418Z","shell.execute_reply":"2022-03-16T17:46:38.575229Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"..........\nEpisode 0/12\n","output_type":"stream"},{"name":"stderr","text":"2022-03-16 17:35:42.116687: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-03-16 17:35:42.291311: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-03-16 17:35:42.292462: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-03-16 17:35:42.294439: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2022-03-16 17:35:42.295866: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-03-16 17:35:42.296924: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-03-16 17:35:42.297984: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-03-16 17:35:44.782407: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-03-16 17:35:44.783664: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-03-16 17:35:44.784781: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-03-16 17:35:44.785766: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n2022-03-16 17:35:45.477991: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","output_type":"stream"},{"name":"stdout","text":"--------------------------------\n","output_type":"stream"},{"name":"stderr","text":"2022-03-16 17:36:10.092039: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n","output_type":"stream"},{"name":"stdout","text":"..........\nEpisode 1/12\n--------------------------------\n..........\nEpisode 2/12\n--------------------------------\n..........\nEpisode 3/12\n--------------------------------\n..........\nEpisode 4/12\n--------------------------------\n..........\nEpisode 5/12\n--------------------------------\n..........\nEpisode 6/12\n--------------------------------\n..........\nEpisode 7/12\n--------------------------------\n..........\nEpisode 8/12\n--------------------------------\n..........\nEpisode 9/12\n--------------------------------\n..........\nEpisode 10/12\n--------------------------------\n..........\nEpisode 11/12\n--------------------------------\n..........\nEpisode 12/12\n--------------------------------\nTotal Apple stocks in episodes: [78, 0, 93, 69, 70, 81, 91, 93, 65, 89, 92, 202, 0]\nTotal Amazon stocks in episodes: [0, 558, 545, 534, 0, 522, 0, 1397, 546, 1441, 528, 555, 549]\nTotal Open cash in episodes: [40387.90199999998, 40848.54099999998, 22516.725999999995, 28166.550000000017, 43229.85599999998, 24406.69800000001, 38464.06699999993, 275.80900000001066, 29322.14099999999, 10.31699999999968, 23847.162, 61.73899999996863, 41122.099999999984]\nTotal Portfolio value in episodes: [59681.98199999998, 56594.74299999999, 98681.291, 90581.98800000001, 57572.85599999998, 92899.45200000002, 58038.16699999993, 66686.861, 90076.26299999999, 61296.692, 98685.258, 66676.82899999997, 56074.66399999998]\nTotal Days in episodes: [791, 277, 2499, 2499, 821, 2499, 807, 1132, 2499, 1152, 2499, 349, 272]\nTotal Profit in episodes: [9221.781999999977, 6435.712999999982, 48681.291, 40581.98800000001, 7137.4559999999765, 42899.45200000002, 7756.06699999993, 16962.796000000002, 40076.26299999999, 11688.385000000002, 48685.258, 16978.68899999997, 6292.067999999985]\nBenchmark Profit is 70032.941 with Stock1: 2.0 and Stock2: 7.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Benchmark model for Actual Test Run","metadata":{"id":"AI70t1w_SeDE"}},{"cell_type":"code","source":"data1_test, data2_test, benchValueTest = benchmark(pd_data1_test, pd_data2_test, test, start_balance)\nprint(f'Benchmark Profit is {benchValueTest[-1][3]} with Stock1: {benchValueTest[-1][1]} and Stock2: {benchValueTest[-1][2]}')","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:46:38.578379Z","iopub.execute_input":"2022-03-16T17:46:38.578731Z","iopub.status.idle":"2022-03-16T17:46:38.589477Z","shell.execute_reply.started":"2022-03-16T17:46:38.578685Z","shell.execute_reply":"2022-03-16T17:46:38.588214Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"Benchmark Profit is 59556.385999999984 with Stock1: 2.0 and Stock2: 5.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Test","metadata":{"id":"PBplUktVSeDF"}},{"cell_type":"code","source":"def qLearningTradingTest(df1, df2, train_size, initial_balance, model_name, agent_train):\n    \"\"\"\n    df1, df2: The test data of each stock\n    train_size: The size (days) of the training\n    initial_balance: The starting wallet balance\n    model_name: The name of the model we will load\n    \"\"\"\n    # Define arrays to store per episode values \n    Act_total_Prof = []\n    Act_total_stock1bal = []\n    Act_total_stock2bal = []\n    Act_total_open_cash = []\n    Act_total_port_value = []\n    Act_total_days_played = []\n    actions_done_perday = []\n    portfolio_value = []\n    \n    model = load_model(\"models/\" + model_name)\n    \n    benchStock1Balance = int(np.floor((initial_balance/4)/df1[0]))\n    benchStock2Balance = int(np.floor((initial_balance/4)/df2[0]))\n    openCash = initial_balance/2\n\n    done = False\n    total_profit = 0\n    reward = 0\n    days_count = 0\n\n    # Initialize Agent\n    agent = Agent(8, is_eval=True, model_name=model_name)\n    agent.inventory1 = []\n    agent.inventory2 = []\n    for i in range(benchStock1Balance):\n        agent.inventory1.append(df1[0])\n    for i in range(benchStock2Balance):\n        agent.inventory2.append(df2[0])\n\n    cash = openCash\n    # Running episode over all days in the train_size\n    for t in range(train_size):\n        print(\"..........\")\n        \n        print(pd_data1_test.iloc[t,0])\n       \n        stateClassObj = State(df1, df2, benchStock1Balance, benchStock2Balance, cash, t)\n        stateArrayObj = stateClassObj.getState()\n        action = agent.act(stateArrayObj)\n        \n        print(f\"Total portfolio value: {stateClassObj.portfolio_value}, stock1 number: {len(agent.inventory1)}, \"\n              + f\"stock2 number: {len(agent.inventory2)} and open cash: {stateClassObj.open_cash}\")\n\n        changePercentStock1 = (stateClassObj.Stock1Price - stateClassObj.fiveday_stock1) / stateClassObj.fiveday_stock1 * 100\n        changePercentStock2 = (stateClassObj.Stock2Price - stateClassObj.fiveday_stock2) / stateClassObj.fiveday_stock2 * 100\n        \n        if action == 0:  # Buy stock 1\n            if stateClassObj.Stock1Price > stateClassObj.open_cash:\n                done = True # end of episode; not enough money to buy\n\n            else:\n                agent.inventory1.append(df1[t])\n                stock1Balance = len(agent.inventory1)\n                stock2Balance = len(agent.inventory2)\n                cash = stateClassObj.open_cash - stateClassObj.Stock1Price # Buy 1 stock\n\n        if action == 1:  # Sell stock 1\n            if stateClassObj.Stock1Blnc < 1:# or len(agent.inventory1) < 1:\n                done = True # end of episode\n\n            else:\n                try:\n                    agent.inventory1.pop(0)\n                except:\n                    agent_train.inventory1.pop(0)\n                stock1Balance = len(agent.inventory1)\n                stock2Balance = len(agent.inventory2)\n                cash = stateClassObj.open_cash + stateClassObj.Stock1Price # Sell 1 stock\n\n\n        if action == 2: # Do nothing action    \n            stock1Balance = len(agent.inventory1)\n            stock2Balance = len(agent.inventory2)\n\n        if action == 3:  # Buy stock 2\n            if stateClassObj.Stock2Price > stateClassObj.open_cash:\n                done = True\n                \n            else:\n                agent.inventory2.append(df2[t])\n                stock1Balance = len(agent.inventory1)\n                stock2Balance = len(agent.inventory2)\n                cash = stateClassObj.open_cash - stateClassObj.Stock2Price # Sell 1 stock\n\n\n        if action == 4:  # Sell stock 2\n            if stateClassObj.Stock2Blnc < 1:# or len(agent.inventory2) < 1:\n                done = True # don't have stock2.\n                \n            else:\n                try:\n                    agent.inventory2.pop(0)\n                except:\n                    agent_train.inventory2.pop(0)\n#                 agent.inventory2.pop(0)\n                stock1Balance = len(agent.inventory1)\n                stock2Balance = len(agent.inventory2)\n                cash = stateClassObj.open_cash + stateClassObj.Stock2Price # Sell 1 stock\n\n        if t == train_size - 1:\n            done = True\n            nextStateClassObj = State(df1, df2, stock1Balance, stock2Balance, cash, t)\n            nextStateArrayObj = nextStateClassObj.getState()\n        else:\n            nextStateClassObj = State(df1, df2, stock1Balance, stock2Balance, cash, t + 1)\n            nextStateArrayObj = nextStateClassObj.getState()\n\n        actions_done_perday.append(action)\n        portfolio_value.append(nextStateClassObj.portfolio_value)\n        \n#         benchStock1Balance = stock1Balance\n#         benchStock2Balance = stock2Balance\n#         openCash = cash\n        \n        if action != 2:\n            days_count += 1\n\n        if done == True:\n            print(\"--------------------------------\")\n            print(f\"Total Profit: {formatPrice(stateClassObj.portfolio_value - initial_balance)}\")\n            print(f\"Total No. of days played: {days_count} out of overall days: {train_size}\")\n            print(f\"Total portfolio value: {stateClassObj.portfolio_value}, stock1 number: {len(agent.inventory1)}, \"\n              + f\"stock2 number: {len(agent.inventory2)} and open cash: {nextStateClassObj.open_cash}\")\n\n            print(\"--------------------------------\")\n            stateClassObj.reset()\n            break\n            \n        \n\n    return portfolio_value","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:46:38.591724Z","iopub.execute_input":"2022-03-16T17:46:38.592073Z","iopub.status.idle":"2022-03-16T17:46:38.622755Z","shell.execute_reply.started":"2022-03-16T17:46:38.592027Z","shell.execute_reply":"2022-03-16T17:46:38.621618Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"portfolio_value = qLearningTradingTest(data1_test, data2_test, test, start_balance, 'model_ep10', agent_train) #'model_ep10'","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:46:38.624411Z","iopub.execute_input":"2022-03-16T17:46:38.625076Z","iopub.status.idle":"2022-03-16T17:47:02.245686Z","shell.execute_reply.started":"2022-03-16T17:46:38.625025Z","shell.execute_reply":"2022-03-16T17:47:02.244588Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"..........\n2015-06-08 00:00:00\nTotal portfolio value: 49560.47, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-09 00:00:00\nTotal portfolio value: 49447.665, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-10 00:00:00\nTotal portfolio value: 49479.675, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-11 00:00:00\nTotal portfolio value: 49731.96, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-12 00:00:00\nTotal portfolio value: 49538.635, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-15 00:00:00\nTotal portfolio value: 49342.285, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-16 00:00:00\nTotal portfolio value: 49293.445, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-17 00:00:00\nTotal portfolio value: 49373.47, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-18 00:00:00\nTotal portfolio value: 49429.35, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-19 00:00:00\nTotal portfolio value: 49681.415, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-22 00:00:00\nTotal portfolio value: 49698.795, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-23 00:00:00\nTotal portfolio value: 49606.505, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-24 00:00:00\nTotal portfolio value: 49555.135, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-25 00:00:00\nTotal portfolio value: 49514.325, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-26 00:00:00\nTotal portfolio value: 49424.62, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-29 00:00:00\nTotal portfolio value: 49079.825, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-06-30 00:00:00\nTotal portfolio value: 49030.93, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-01 00:00:00\nTotal portfolio value: 48983.52, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-02 00:00:00\nTotal portfolio value: 48989.130000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-06 00:00:00\nTotal portfolio value: 48797.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-07 00:00:00\nTotal portfolio value: 49038.685, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-08 00:00:00\nTotal portfolio value: 49031.425, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-09 00:00:00\nTotal portfolio value: 49227.39, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-10 00:00:00\nTotal portfolio value: 49441.945, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-13 00:00:00\nTotal portfolio value: 49719.145000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-14 00:00:00\nTotal portfolio value: 49976.325, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-15 00:00:00\nTotal portfolio value: 50378.43, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-16 00:00:00\nTotal portfolio value: 50514.94, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-17 00:00:00\nTotal portfolio value: 52547.575, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-20 00:00:00\nTotal portfolio value: 52696.185, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-21 00:00:00\nTotal portfolio value: 52572.71, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-22 00:00:00\nTotal portfolio value: 52698.99, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-23 00:00:00\nTotal portfolio value: 52532.725, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-24 00:00:00\nTotal portfolio value: 52585.91, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-27 00:00:00\nTotal portfolio value: 51896.54, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-28 00:00:00\nTotal portfolio value: 52373.17, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-29 00:00:00\nTotal portfolio value: 52528.82, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-30 00:00:00\nTotal portfolio value: 52492.08, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-07-31 00:00:00\nTotal portfolio value: 52881.700000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-03 00:00:00\nTotal portfolio value: 52628.645, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-04 00:00:00\nTotal portfolio value: 52728.855, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-05 00:00:00\nTotal portfolio value: 52847.875, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-06 00:00:00\nTotal portfolio value: 53129.53, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-07 00:00:00\nTotal portfolio value: 52827.91, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-10 00:00:00\nTotal portfolio value: 52830.880000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-11 00:00:00\nTotal portfolio value: 53540.435, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-12 00:00:00\nTotal portfolio value: 53487.855, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-13 00:00:00\nTotal portfolio value: 53495.5, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-14 00:00:00\nTotal portfolio value: 53443.58, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-17 00:00:00\nTotal portfolio value: 53521.13, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-18 00:00:00\nTotal portfolio value: 53747.565, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-19 00:00:00\nTotal portfolio value: 53698.12, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-20 00:00:00\nTotal portfolio value: 53639.875, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-21 00:00:00\nTotal portfolio value: 53182.88, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-24 00:00:00\nTotal portfolio value: 50878.325, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-25 00:00:00\nTotal portfolio value: 52229.29, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-26 00:00:00\nTotal portfolio value: 52119.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-27 00:00:00\nTotal portfolio value: 52680.73, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-28 00:00:00\nTotal portfolio value: 52556.595, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-08-31 00:00:00\nTotal portfolio value: 52318.28, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-01 00:00:00\nTotal portfolio value: 51657.619999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-02 00:00:00\nTotal portfolio value: 51858.425, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-03 00:00:00\nTotal portfolio value: 52112.91, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-04 00:00:00\nTotal portfolio value: 51662.625, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-08 00:00:00\nTotal portfolio value: 52140.85, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-09 00:00:00\nTotal portfolio value: 52493.785, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-10 00:00:00\nTotal portfolio value: 52097.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-11 00:00:00\nTotal portfolio value: 52210.645000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-14 00:00:00\nTotal portfolio value: 52415.135, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-15 00:00:00\nTotal portfolio value: 52449.62, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-16 00:00:00\nTotal portfolio value: 52706.25, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-17 00:00:00\nTotal portfolio value: 52736.28, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-18 00:00:00\nTotal portfolio value: 52480.145, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-21 00:00:00\nTotal portfolio value: 52698.22, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-22 00:00:00\nTotal portfolio value: 52362.115000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-23 00:00:00\nTotal portfolio value: 52321.8, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-24 00:00:00\nTotal portfolio value: 52177.865, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-25 00:00:00\nTotal portfolio value: 52534.76, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-28 00:00:00\nTotal portfolio value: 52094.65, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-29 00:00:00\nTotal portfolio value: 51677.2, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-09-30 00:00:00\nTotal portfolio value: 51779.885, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-01 00:00:00\nTotal portfolio value: 51984.43, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-02 00:00:00\nTotal portfolio value: 51947.47, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-05 00:00:00\nTotal portfolio value: 52762.625, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-06 00:00:00\nTotal portfolio value: 53081.350000000006, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-07 00:00:00\nTotal portfolio value: 53297.225, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-08 00:00:00\nTotal portfolio value: 53184.75, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-09 00:00:00\nTotal portfolio value: 53440.28, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-12 00:00:00\nTotal portfolio value: 53508.755000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-13 00:00:00\nTotal portfolio value: 53662.369999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-14 00:00:00\nTotal portfolio value: 53803.83, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-15 00:00:00\nTotal portfolio value: 53727.05, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-16 00:00:00\nTotal portfolio value: 53978.675, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-19 00:00:00\nTotal portfolio value: 53873.075, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-20 00:00:00\nTotal portfolio value: 53979.005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-21 00:00:00\nTotal portfolio value: 53871.92, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-22 00:00:00\nTotal portfolio value: 53851.405, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-23 00:00:00\nTotal portfolio value: 55425.67, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-26 00:00:00\nTotal portfolio value: 54676.515, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-27 00:00:00\nTotal portfolio value: 54737.565, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-28 00:00:00\nTotal portfolio value: 55307.475, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-29 00:00:00\nTotal portfolio value: 55408.235, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-10-30 00:00:00\nTotal portfolio value: 55544.744999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-02 00:00:00\nTotal portfolio value: 55225.085, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-03 00:00:00\nTotal portfolio value: 55508.335, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-04 00:00:00\nTotal portfolio value: 55549.2, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-05 00:00:00\nTotal portfolio value: 55815.619999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-06 00:00:00\nTotal portfolio value: 55846.53, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-09 00:00:00\nTotal portfolio value: 55785.865, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-10 00:00:00\nTotal portfolio value: 55492.935, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-11 00:00:00\nTotal portfolio value: 55733.01, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-12 00:00:00\nTotal portfolio value: 55612.01, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-13 00:00:00\nTotal portfolio value: 55266.555, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-16 00:00:00\nTotal portfolio value: 54806.37, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-17 00:00:00\nTotal portfolio value: 55224.04, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-18 00:00:00\nTotal portfolio value: 55199.29, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-19 00:00:00\nTotal portfolio value: 55683.509999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-20 00:00:00\nTotal portfolio value: 55916.325, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-23 00:00:00\nTotal portfolio value: 56163.715, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-24 00:00:00\nTotal portfolio value: 55999.595, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-25 00:00:00\nTotal portfolio value: 56010.705, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-27 00:00:00\nTotal portfolio value: 55947.345, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-11-30 00:00:00\nTotal portfolio value: 56017.14, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-01 00:00:00\nTotal portfolio value: 55914.18, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-02 00:00:00\nTotal portfolio value: 56286.475, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-03 00:00:00\nTotal portfolio value: 56176.145000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-04 00:00:00\nTotal portfolio value: 55541.72, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-07 00:00:00\nTotal portfolio value: 55887.395, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-08 00:00:00\nTotal portfolio value: 55385.575, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-09 00:00:00\nTotal portfolio value: 55648.475000000006, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-10 00:00:00\nTotal portfolio value: 55396.905, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-11 00:00:00\nTotal portfolio value: 54986.55, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-14 00:00:00\nTotal portfolio value: 54999.97, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-15 00:00:00\nTotal portfolio value: 55523.405, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-16 00:00:00\nTotal portfolio value: 55914.84, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-17 00:00:00\nTotal portfolio value: 56291.095, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-18 00:00:00\nTotal portfolio value: 55746.21, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-21 00:00:00\nTotal portfolio value: 55649.024999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-22 00:00:00\nTotal portfolio value: 55678.34, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-23 00:00:00\nTotal portfolio value: 55929.58, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-24 00:00:00\nTotal portfolio value: 55897.79, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-28 00:00:00\nTotal portfolio value: 55911.925, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-29 00:00:00\nTotal portfolio value: 56447.955, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-30 00:00:00\nTotal portfolio value: 56682.42, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2015-12-31 00:00:00\nTotal portfolio value: 56416.44, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-04 00:00:00\nTotal portfolio value: 55531.05, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-05 00:00:00\nTotal portfolio value: 55525.55, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-06 00:00:00\nTotal portfolio value: 55158.315, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-07 00:00:00\nTotal portfolio value: 55179.93, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-08 00:00:00\nTotal portfolio value: 55237.899999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-11 00:00:00\nTotal portfolio value: 54800.100000000006, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-12 00:00:00\nTotal portfolio value: 55155.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-13 00:00:00\nTotal portfolio value: 55402.68, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-14 00:00:00\nTotal portfolio value: 54658.53, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-15 00:00:00\nTotal portfolio value: 54204.78, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-19 00:00:00\nTotal portfolio value: 54446.725, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-20 00:00:00\nTotal portfolio value: 54125.25, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-21 00:00:00\nTotal portfolio value: 54423.24, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-22 00:00:00\nTotal portfolio value: 55068.5, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-25 00:00:00\nTotal portfolio value: 55211.28, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-26 00:00:00\nTotal portfolio value: 54830.020000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-27 00:00:00\nTotal portfolio value: 54829.58, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-28 00:00:00\nTotal portfolio value: 54846.08, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-01-29 00:00:00\nTotal portfolio value: 55164.145000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-01 00:00:00\nTotal portfolio value: 55754.020000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-02 00:00:00\nTotal portfolio value: 56399.775, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-03 00:00:00\nTotal portfolio value: 56026.985, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-04 00:00:00\nTotal portfolio value: 55227.78, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-05 00:00:00\nTotal portfolio value: 54810.275, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-08 00:00:00\nTotal portfolio value: 53794.81, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-09 00:00:00\nTotal portfolio value: 53910.75, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-10 00:00:00\nTotal portfolio value: 54655.229999999996, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-11 00:00:00\nTotal portfolio value: 54074.43, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-12 00:00:00\nTotal portfolio value: 54598.745, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-16 00:00:00\nTotal portfolio value: 55127.9, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-17 00:00:00\nTotal portfolio value: 55350.925, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-18 00:00:00\nTotal portfolio value: 55485.62, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-19 00:00:00\nTotal portfolio value: 55205.725, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-22 00:00:00\nTotal portfolio value: 55638.795, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-23 00:00:00\nTotal portfolio value: 55673.225, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-24 00:00:00\nTotal portfolio value: 55296.145000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-25 00:00:00\nTotal portfolio value: 55622.185, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-26 00:00:00\nTotal portfolio value: 55917.15, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-02-29 00:00:00\nTotal portfolio value: 55542.875, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-01 00:00:00\nTotal portfolio value: 55570.375, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-02 00:00:00\nTotal portfolio value: 56217.89, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-03 00:00:00\nTotal portfolio value: 56216.46, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-04 00:00:00\nTotal portfolio value: 56253.75, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-07 00:00:00\nTotal portfolio value: 55967.2, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-08 00:00:00\nTotal portfolio value: 55540.73, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-09 00:00:00\nTotal portfolio value: 55871.83, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-10 00:00:00\nTotal portfolio value: 56238.955, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-11 00:00:00\nTotal portfolio value: 56564.5, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-14 00:00:00\nTotal portfolio value: 56777.240000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-15 00:00:00\nTotal portfolio value: 56650.74, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-16 00:00:00\nTotal portfolio value: 56920.075, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-17 00:00:00\nTotal portfolio value: 57190.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-18 00:00:00\nTotal portfolio value: 57268.06, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-21 00:00:00\nTotal portfolio value: 56981.895000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-22 00:00:00\nTotal portfolio value: 57105.755000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-23 00:00:00\nTotal portfolio value: 57197.825, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-24 00:00:00\nTotal portfolio value: 57009.45, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-28 00:00:00\nTotal portfolio value: 57213.39, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-29 00:00:00\nTotal portfolio value: 57166.86, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-30 00:00:00\nTotal portfolio value: 57644.645000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-03-31 00:00:00\nTotal portfolio value: 57589.755, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-01 00:00:00\nTotal portfolio value: 57164.495, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-04 00:00:00\nTotal portfolio value: 57494.22, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-05 00:00:00\nTotal portfolio value: 57029.91, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-06 00:00:00\nTotal portfolio value: 57015.555, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-07 00:00:00\nTotal portfolio value: 56990.64, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-08 00:00:00\nTotal portfolio value: 57092.39, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-11 00:00:00\nTotal portfolio value: 57083.15, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-12 00:00:00\nTotal portfolio value: 56804.685, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-13 00:00:00\nTotal portfolio value: 57060.545, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-14 00:00:00\nTotal portfolio value: 57082.545, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-15 00:00:00\nTotal portfolio value: 57104.325, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-18 00:00:00\nTotal portfolio value: 57170.104999999996, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-19 00:00:00\nTotal portfolio value: 57481.9, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-20 00:00:00\nTotal portfolio value: 57237.37, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-21 00:00:00\nTotal portfolio value: 57017.095, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-22 00:00:00\nTotal portfolio value: 56208.595, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-25 00:00:00\nTotal portfolio value: 55988.925, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-26 00:00:00\nTotal portfolio value: 56254.19, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-27 00:00:00\nTotal portfolio value: 55870.565, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-28 00:00:00\nTotal portfolio value: 55776.405, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-04-29 00:00:00\nTotal portfolio value: 55881.29, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-02 00:00:00\nTotal portfolio value: 56211.29, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-03 00:00:00\nTotal portfolio value: 56419.025, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-04 00:00:00\nTotal portfolio value: 56319.09, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-05 00:00:00\nTotal portfolio value: 56721.525, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-06 00:00:00\nTotal portfolio value: 56557.075000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-09 00:00:00\nTotal portfolio value: 56984.15, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-10 00:00:00\nTotal portfolio value: 57303.095, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-11 00:00:00\nTotal portfolio value: 57446.865, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-12 00:00:00\nTotal portfolio value: 57259.425, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-13 00:00:00\nTotal portfolio value: 57235.665, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-16 00:00:00\nTotal portfolio value: 57076.715000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-17 00:00:00\nTotal portfolio value: 57230.494999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-18 00:00:00\nTotal portfolio value: 56819.975, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-19 00:00:00\nTotal portfolio value: 56781.2, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-20 00:00:00\nTotal portfolio value: 56856.495, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-23 00:00:00\nTotal portfolio value: 56844.285, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-24 00:00:00\nTotal portfolio value: 56902.475, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-25 00:00:00\nTotal portfolio value: 57258.875, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-26 00:00:00\nTotal portfolio value: 57186.0, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-27 00:00:00\nTotal portfolio value: 57333.07, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-05-31 00:00:00\nTotal portfolio value: 57638.32, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-01 00:00:00\nTotal portfolio value: 57547.79, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-02 00:00:00\nTotal portfolio value: 57599.05, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-03 00:00:00\nTotal portfolio value: 57603.78, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-06 00:00:00\nTotal portfolio value: 57540.75, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-07 00:00:00\nTotal portfolio value: 57486.74, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-08 00:00:00\nTotal portfolio value: 57764.05, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-09 00:00:00\nTotal portfolio value: 57676.490000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-10 00:00:00\nTotal portfolio value: 57805.850000000006, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-13 00:00:00\nTotal portfolio value: 57676.215, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-14 00:00:00\nTotal portfolio value: 57620.17, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-15 00:00:00\nTotal portfolio value: 57918.215, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-16 00:00:00\nTotal portfolio value: 57669.67, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-17 00:00:00\nTotal portfolio value: 57687.105, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-20 00:00:00\nTotal portfolio value: 57315.744999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-21 00:00:00\nTotal portfolio value: 57315.524999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-22 00:00:00\nTotal portfolio value: 57411.5, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-23 00:00:00\nTotal portfolio value: 57456.6, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-24 00:00:00\nTotal portfolio value: 56625.99, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-27 00:00:00\nTotal portfolio value: 56863.205, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-28 00:00:00\nTotal portfolio value: 57124.565, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-29 00:00:00\nTotal portfolio value: 57387.52, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-06-30 00:00:00\nTotal portfolio value: 57560.274999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-01 00:00:00\nTotal portfolio value: 58063.525, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-05 00:00:00\nTotal portfolio value: 58109.395, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-06 00:00:00\nTotal portfolio value: 58213.455, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-07 00:00:00\nTotal portfolio value: 58478.995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-08 00:00:00\nTotal portfolio value: 58526.020000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-11 00:00:00\nTotal portfolio value: 58765.765, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-12 00:00:00\nTotal portfolio value: 59141.965000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-13 00:00:00\nTotal portfolio value: 59119.965, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-14 00:00:00\nTotal portfolio value: 59220.505000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-15 00:00:00\nTotal portfolio value: 59360.149999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-18 00:00:00\nTotal portfolio value: 59217.645, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-19 00:00:00\nTotal portfolio value: 59351.240000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-20 00:00:00\nTotal portfolio value: 59514.149999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-21 00:00:00\nTotal portfolio value: 59598.025, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-22 00:00:00\nTotal portfolio value: 59512.39, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-25 00:00:00\nTotal portfolio value: 59464.21, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-26 00:00:00\nTotal portfolio value: 59519.54, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-27 00:00:00\nTotal portfolio value: 59691.965, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-28 00:00:00\nTotal portfolio value: 59537.58, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-07-29 00:00:00\nTotal portfolio value: 60156.495, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-01 00:00:00\nTotal portfolio value: 59884.74, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-02 00:00:00\nTotal portfolio value: 60134.935, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-03 00:00:00\nTotal portfolio value: 60039.29, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-04 00:00:00\nTotal portfolio value: 60072.729999999996, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-05 00:00:00\nTotal portfolio value: 60127.07000000001, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-08 00:00:00\nTotal portfolio value: 60238.225, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-09 00:00:00\nTotal portfolio value: 60146.705, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-10 00:00:00\nTotal portfolio value: 60248.399999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-11 00:00:00\nTotal portfolio value: 60519.44, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-12 00:00:00\nTotal portfolio value: 60345.805, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-15 00:00:00\nTotal portfolio value: 60442.770000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-16 00:00:00\nTotal portfolio value: 60185.975, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-17 00:00:00\nTotal portfolio value: 59896.675, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-18 00:00:00\nTotal portfolio value: 60033.295, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-19 00:00:00\nTotal portfolio value: 59782.88, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-22 00:00:00\nTotal portfolio value: 59813.57, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-23 00:00:00\nTotal portfolio value: 59991.935, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-24 00:00:00\nTotal portfolio value: 59835.845, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-25 00:00:00\nTotal portfolio value: 59760.274999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-26 00:00:00\nTotal portfolio value: 59678.38, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-29 00:00:00\nTotal portfolio value: 59454.475, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-30 00:00:00\nTotal portfolio value: 59562.384999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-08-31 00:00:00\nTotal portfolio value: 59320.275, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-01 00:00:00\nTotal portfolio value: 59399.31, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-02 00:00:00\nTotal portfolio value: 59561.89, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-06 00:00:00\nTotal portfolio value: 59681.13, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-07 00:00:00\nTotal portfolio value: 60027.41, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-08 00:00:00\nTotal portfolio value: 59900.415, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-09 00:00:00\nTotal portfolio value: 59737.34, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-12 00:00:00\nTotal portfolio value: 59160.115, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-13 00:00:00\nTotal portfolio value: 59398.595, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-14 00:00:00\nTotal portfolio value: 59196.91, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-15 00:00:00\nTotal portfolio value: 59197.57000000001, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-16 00:00:00\nTotal portfolio value: 59377.09, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-19 00:00:00\nTotal portfolio value: 59463.22, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-20 00:00:00\nTotal portfolio value: 59659.35, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-21 00:00:00\nTotal portfolio value: 59654.895000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-22 00:00:00\nTotal portfolio value: 60072.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-23 00:00:00\nTotal portfolio value: 59895.63, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-26 00:00:00\nTotal portfolio value: 59829.740000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-27 00:00:00\nTotal portfolio value: 59761.485, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-28 00:00:00\nTotal portfolio value: 59877.81, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-29 00:00:00\nTotal portfolio value: 59885.785, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-09-30 00:00:00\nTotal portfolio value: 59741.3, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-03 00:00:00\nTotal portfolio value: 59774.85, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-04 00:00:00\nTotal portfolio value: 59766.6, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-05 00:00:00\nTotal portfolio value: 59613.975000000006, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-06 00:00:00\nTotal portfolio value: 59451.01, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-07 00:00:00\nTotal portfolio value: 59588.235, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-10 00:00:00\nTotal portfolio value: 59439.46, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-11 00:00:00\nTotal portfolio value: 59737.34, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-12 00:00:00\nTotal portfolio value: 59670.020000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-13 00:00:00\nTotal portfolio value: 59421.915, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-14 00:00:00\nTotal portfolio value: 59385.45, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-17 00:00:00\nTotal portfolio value: 59358.55499999999, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-18 00:00:00\nTotal portfolio value: 59856.47, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-19 00:00:00\nTotal portfolio value: 59948.815, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-20 00:00:00\nTotal portfolio value: 59975.545, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-21 00:00:00\nTotal portfolio value: 59707.2, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-24 00:00:00\nTotal portfolio value: 60031.205, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-25 00:00:00\nTotal portfolio value: 60237.399999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-26 00:00:00\nTotal portfolio value: 60406.69, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-27 00:00:00\nTotal portfolio value: 60356.42, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-28 00:00:00\nTotal portfolio value: 60326.28, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-10-31 00:00:00\nTotal portfolio value: 60542.485, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-01 00:00:00\nTotal portfolio value: 60285.69, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-02 00:00:00\nTotal portfolio value: 59966.744999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-03 00:00:00\nTotal portfolio value: 59361.25, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-04 00:00:00\nTotal portfolio value: 59008.975, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-07 00:00:00\nTotal portfolio value: 59758.075, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-08 00:00:00\nTotal portfolio value: 60026.31, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-09 00:00:00\nTotal portfolio value: 60021.91, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-10 00:00:00\nTotal portfolio value: 60556.399999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-11 00:00:00\nTotal portfolio value: 59933.744999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-14 00:00:00\nTotal portfolio value: 60158.695, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-15 00:00:00\nTotal portfolio value: 60071.465, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-16 00:00:00\nTotal portfolio value: 60400.64, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-17 00:00:00\nTotal portfolio value: 60644.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-18 00:00:00\nTotal portfolio value: 60795.815, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-21 00:00:00\nTotal portfolio value: 60658.15, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-22 00:00:00\nTotal portfolio value: 61080.33, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-23 00:00:00\nTotal portfolio value: 61057.34, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-25 00:00:00\nTotal portfolio value: 60919.07000000001, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-28 00:00:00\nTotal portfolio value: 60935.075, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-29 00:00:00\nTotal portfolio value: 61160.96000000001, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-11-30 00:00:00\nTotal portfolio value: 61379.2, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-01 00:00:00\nTotal portfolio value: 60946.899999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-02 00:00:00\nTotal portfolio value: 60570.15, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-05 00:00:00\nTotal portfolio value: 60834.7, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-06 00:00:00\nTotal portfolio value: 60785.805, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-07 00:00:00\nTotal portfolio value: 60934.525, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-08 00:00:00\nTotal portfolio value: 61342.350000000006, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-09 00:00:00\nTotal portfolio value: 61525.225, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-12 00:00:00\nTotal portfolio value: 61611.465, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-13 00:00:00\nTotal portfolio value: 61999.655, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-14 00:00:00\nTotal portfolio value: 62150.465, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-15 00:00:00\nTotal portfolio value: 61873.869999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-16 00:00:00\nTotal portfolio value: 62013.57, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-19 00:00:00\nTotal portfolio value: 61925.735, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-20 00:00:00\nTotal portfolio value: 62153.490000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-21 00:00:00\nTotal portfolio value: 62164.765, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-22 00:00:00\nTotal portfolio value: 61908.575, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-23 00:00:00\nTotal portfolio value: 61957.47, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-27 00:00:00\nTotal portfolio value: 61972.21, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-28 00:00:00\nTotal portfolio value: 62085.235, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-29 00:00:00\nTotal portfolio value: 61843.235, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2016-12-30 00:00:00\nTotal portfolio value: 61935.47, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-03 00:00:00\nTotal portfolio value: 61805.615000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-04 00:00:00\nTotal portfolio value: 61941.905, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-05 00:00:00\nTotal portfolio value: 61786.75, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-06 00:00:00\nTotal portfolio value: 61962.255000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-09 00:00:00\nTotal portfolio value: 62196.39, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-10 00:00:00\nTotal portfolio value: 62003.89, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-11 00:00:00\nTotal portfolio value: 61961.265, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-12 00:00:00\nTotal portfolio value: 62135.285, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-13 00:00:00\nTotal portfolio value: 62233.350000000006, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-17 00:00:00\nTotal portfolio value: 62057.35, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-18 00:00:00\nTotal portfolio value: 62022.975, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-19 00:00:00\nTotal portfolio value: 61989.425, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-20 00:00:00\nTotal portfolio value: 62086.005000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-23 00:00:00\nTotal portfolio value: 62133.47, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-24 00:00:00\nTotal portfolio value: 62379.76, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-25 00:00:00\nTotal portfolio value: 62580.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-26 00:00:00\nTotal portfolio value: 62737.149999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-27 00:00:00\nTotal portfolio value: 62779.225000000006, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-30 00:00:00\nTotal portfolio value: 62253.369999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-01-31 00:00:00\nTotal portfolio value: 61872.275, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-01 00:00:00\nTotal portfolio value: 61865.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-02 00:00:00\nTotal portfolio value: 61622.025, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-03 00:00:00\nTotal portfolio value: 61971.11, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-06 00:00:00\nTotal portfolio value: 61930.465, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-07 00:00:00\nTotal portfolio value: 61942.125, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-08 00:00:00\nTotal portfolio value: 62152.885, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-09 00:00:00\nTotal portfolio value: 62190.009999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-10 00:00:00\nTotal portfolio value: 62289.725, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-13 00:00:00\nTotal portfolio value: 62510.55, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-14 00:00:00\nTotal portfolio value: 62610.265, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-15 00:00:00\nTotal portfolio value: 62548.445, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-16 00:00:00\nTotal portfolio value: 62271.3, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-17 00:00:00\nTotal portfolio value: 62746.72, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-21 00:00:00\nTotal portfolio value: 63212.68, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-22 00:00:00\nTotal portfolio value: 63348.2, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-23 00:00:00\nTotal portfolio value: 63494.06, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-24 00:00:00\nTotal portfolio value: 63375.425, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-27 00:00:00\nTotal portfolio value: 63340.5, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-02-28 00:00:00\nTotal portfolio value: 63431.25, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-01 00:00:00\nTotal portfolio value: 63557.58500000001, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-02 00:00:00\nTotal portfolio value: 63611.869999999995, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-03 00:00:00\nTotal portfolio value: 63482.18, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-06 00:00:00\nTotal portfolio value: 63249.97, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-07 00:00:00\nTotal portfolio value: 63331.92, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-08 00:00:00\nTotal portfolio value: 63402.54, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-09 00:00:00\nTotal portfolio value: 63415.08, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-10 00:00:00\nTotal portfolio value: 63702.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-13 00:00:00\nTotal portfolio value: 63858.16, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-14 00:00:00\nTotal portfolio value: 63875.65, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-15 00:00:00\nTotal portfolio value: 63951.33, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-16 00:00:00\nTotal portfolio value: 64177.159999999996, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-17 00:00:00\nTotal portfolio value: 64167.535, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-20 00:00:00\nTotal portfolio value: 64134.81, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-21 00:00:00\nTotal portfolio value: 64085.145000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-22 00:00:00\nTotal portfolio value: 63477.56, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-23 00:00:00\nTotal portfolio value: 63239.630000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-24 00:00:00\nTotal portfolio value: 63264.05, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-27 00:00:00\nTotal portfolio value: 62843.630000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-28 00:00:00\nTotal portfolio value: 62946.479999999996, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-29 00:00:00\nTotal portfolio value: 63076.5, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-30 00:00:00\nTotal portfolio value: 63371.96000000001, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-03-31 00:00:00\nTotal portfolio value: 63402.76, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-03 00:00:00\nTotal portfolio value: 63542.9, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-04 00:00:00\nTotal portfolio value: 63414.75, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-05 00:00:00\nTotal portfolio value: 63592.62, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-06 00:00:00\nTotal portfolio value: 63578.649999999994, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-07 00:00:00\nTotal portfolio value: 63490.375, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-10 00:00:00\nTotal portfolio value: 63392.53, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-11 00:00:00\nTotal portfolio value: 63333.350000000006, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-12 00:00:00\nTotal portfolio value: 63215.87, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-13 00:00:00\nTotal portfolio value: 63112.03, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-17 00:00:00\nTotal portfolio value: 63147.01, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-18 00:00:00\nTotal portfolio value: 63441.479999999996, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-19 00:00:00\nTotal portfolio value: 63605.270000000004, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-20 00:00:00\nTotal portfolio value: 63727.479999999996, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-21 00:00:00\nTotal portfolio value: 63755.09, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-24 00:00:00\nTotal portfolio value: 64177.380000000005, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-25 00:00:00\nTotal portfolio value: 64584.545, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-26 00:00:00\nTotal portfolio value: 64752.955, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-27 00:00:00\nTotal portfolio value: 64384.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-04-28 00:00:00\nTotal portfolio value: 65227.0, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-01 00:00:00\nTotal portfolio value: 65201.7, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-02 00:00:00\nTotal portfolio value: 65437.53999999999, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-03 00:00:00\nTotal portfolio value: 65395.575, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-04 00:00:00\nTotal portfolio value: 65768.53, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-05 00:00:00\nTotal portfolio value: 65904.49, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-08 00:00:00\nTotal portfolio value: 65812.2, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-09 00:00:00\nTotal portfolio value: 66027.91, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-10 00:00:00\nTotal portfolio value: 65785.14, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-11 00:00:00\nTotal portfolio value: 65736.08, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-12 00:00:00\nTotal portfolio value: 65858.95, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-15 00:00:00\nTotal portfolio value: 65552.38, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-16 00:00:00\nTotal portfolio value: 65823.75, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-17 00:00:00\nTotal portfolio value: 65418.4, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-18 00:00:00\nTotal portfolio value: 64962.725000000006, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-19 00:00:00\nTotal portfolio value: 65234.315, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-22 00:00:00\nTotal portfolio value: 65271.825, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-23 00:00:00\nTotal portfolio value: 65679.32, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-24 00:00:00\nTotal portfolio value: 65842.12, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-25 00:00:00\nTotal portfolio value: 65976.1, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-26 00:00:00\nTotal portfolio value: 66229.65, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-30 00:00:00\nTotal portfolio value: 66351.75, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n..........\n2017-05-31 00:00:00\nTotal portfolio value: 66534.02, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n--------------------------------\nTotal Profit: $16534.02\nTotal No. of days played: 0 out of overall days: 500\nTotal portfolio value: 66534.02, stock1 number: 22, stock2 number: 275 and open cash: 25000.0\n--------------------------------\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Plot Portfolio Value after 1) Training and Testing vs 2) Benchmarking over Test Data","metadata":{}},{"cell_type":"code","source":"# Test Stock Prices and actions taken by agent Stock Plot\nimport matplotlib.pyplot as plt\nimport datetime\nimport numpy as np\n\n%matplotlib inline\n\npd_bm=pd.DataFrame.from_records(benchValueTest) #Benchmark_Port_Value\npd_bm[0]=pd.to_datetime(pd_bm[0], format='%Y/%m/%d')\n\nx1 = np.array(pd_data1_test['Date'])\ny1 = portfolio_value\n\nx2=pd_bm[0]\ny2=pd_bm[3] #TotalPortfolioValue (pd_bm[1] in the old version)\n\n\n\nplt.title(\"Portfolio Value vs Benchmark Over Test Data\")\nplt.xlabel(\"Days\")\nplt.ylabel(\"Portfolio Value in $\")\n\nplt.plot(x1,y1)\nplt.plot_date(x2, y2, c = 'red', marker='v', linestyle='-')\n\n\n#plt.plot(x1, z, '-', color='black');\nplt.plot(x1, y1, '-', color='blue');\n\n\n\n#plt.scatter(x1,Benchmark_Port_Value,marker='o')\n\nplt.legend(('Trading Model', 'Benchmark'))\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-03-16T17:47:02.247943Z","iopub.execute_input":"2022-03-16T17:47:02.248600Z","iopub.status.idle":"2022-03-16T17:47:02.560820Z","shell.execute_reply.started":"2022-03-16T17:47:02.248532Z","shell.execute_reply":"2022-03-16T17:47:02.559957Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:24: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"o\" (-> marker='o'). The keyword argument will take precedence.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]}]}