```python
%conda info -e
#%pip install yfinance
%conda env export > requirements.yml

```

    # conda environments:
    #
    base                     C:\ProgramData\Anaconda3
    tensorflow1              C:\ProgramData\Anaconda3\envs\tensorflow1
    wordcount                C:\ProgramData\Anaconda3\envs\wordcount
    fortran                  C:\Users\salmasi\.conda\envs\fortran
    tensorflow_1.15       *  C:\Users\salmasi\.conda\envs\tensorflow_1.15

    Note: you may need to restart the kernel to use updated packages.

    Note: you may need to restart the kernel to use updated packages.

```python
import numpy as np
import sympy as sym
import matplotlib.pyplot as plt
import scipy as scp
from math import erf
import yfinance as yf
import collections
from pandas_datareader import data as pdr
import numpy as np
from sklearn.preprocessing import scale
from TFANN import ANNR
import pandas as pd
```

    C:\Users\salmasi\.conda\envs\tensorflow_1.15\lib\site-packages\pandas_datareader\compat\__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
      from pandas.util.testing import assert_frame_equal

```python
#aapl = yf.Ticker("aapl")
aapl = yf.Ticker("ibm")

#print(aapl)
print(aapl.info)
aapl.history(period="max")
```

    {'zip': '10504', 'sector': 'Technology', 'fullTimeEmployees': 352600, 'longBusinessSummary': "International Business Machines Corporation operates as an integrated solutions and services company worldwide. Its Cloud & Cognitive Software segment offers software for vertical and domain-specific solutions in health, financial services, and Internet of Things (IoT) application areas; and customer information control system and storage, and analytics and integration software to support client mission critical on-premise workloads in banking, airline, and retail industries. It also offers middleware and data platform software, including Red Hat, which enables the operation of clients' hybrid multi-cloud environments; and Cloud Paks, WebSphere distributed, and analytics platform software, such as DB2 distributed, information integration, and enterprise content management, as well as IoT, Blockchain and AI/Watson platforms. The company's Global Business Services segment offers business consulting services; system integration, application management, maintenance, and support services for packaged software; finance, procurement, talent and engagement, and industry-specific business process outsourcing; and IT infrastructure and platform services. Its Global Technology Services segment provides project, managed, outsourcing, and cloud-delivered services for enterprise IT infrastructure environments; and IT infrastructure support services. The company's Systems segment offers servers for businesses, cloud service providers, and scientific computing organizations; data storage products and solutions; and z/OS, an enterprise operating system, as well as Linux. Its Global Financing segment provides lease, installment payment, loan financing, short-term working capital, and remanufacturing and remarketing services. It has collaboration with Intel. The company was formerly known as Computing-Tabulating-Recording Co. and changed its name to International Business Machines Corporation in 1924. The company was incorporated in 1911 and is headquartered in Armonk, New York.", 'city': 'Armonk', 'phone': '914 499 1900', 'state': 'NY', 'country': 'United States', 'companyOfficers': [], 'website': 'http://www.ibm.com', 'maxAge': 1, 'address1': 'One New Orchard Road', 'industry': 'Information Technology Services', 'previousClose': 129.87, 'regularMarketOpen': 126.03, 'twoHundredDayAverage': 128.72029, 'trailingAnnualDividendYield': 0.04989605, 'payoutRatio': 0.6416, 'volume24Hr': None, 'regularMarketDayHigh': 126.1, 'navPrice': None, 'averageDailyVolume10Day': 5488387, 'totalAssets': None, 'regularMarketPreviousClose': 129.87, 'fiftyDayAverage': 123.78286, 'trailingAnnualDividendRate': 6.48, 'open': 126.03, 'toCurrency': None, 'averageVolume10days': 5488387, 'expireDate': None, 'yield': None, 'algorithm': None, 'dividendRate': 6.52, 'exDividendDate': 1588809600, 'beta': 1.227247, 'circulatingSupply': None, 'startDate': None, 'regularMarketDayLow': 117.8352, 'priceHint': 2, 'currency': 'USD', 'trailingPE': 11.688788, 'regularMarketVolume': 11671007, 'lastMarket': None, 'maxSupply': None, 'openInterest': None, 'marketCap': 104780136448, 'volumeAllCurrencies': None, 'strikePrice': None, 'averageVolume': 6168070, 'priceToSalesTrailing12Months': 1.3690308, 'dayLow': 117.8352, 'ask': 121.16, 'ytdReturn': None, 'askSize': 800, 'volume': 11671007, 'fiftyTwoWeekHigh': 158.75, 'forwardPE': 9.509267, 'fromCurrency': None, 'fiveYearAvgDividendYield': 4.04, 'fiftyTwoWeekLow': 90.56, 'bid': 120.25, 'tradeable': False, 'dividendYield': 0.0502, 'bidSize': 800, 'dayHigh': 126.1, 'exchange': 'NYQ', 'shortName': 'International Business Machines', 'longName': 'International Business Machines Corporation', 'exchangeTimezoneName': 'America/New_York', 'exchangeTimezoneShortName': 'EDT', 'isEsgPopulated': False, 'gmtOffSetMilliseconds': '-14400000', 'quoteType': 'EQUITY', 'symbol': 'IBM', 'messageBoardId': 'finmb_112350', 'market': 'us_market', 'annualHoldingsTurnover': None, 'enterpriseToRevenue': 2.261, 'beta3Year': None, 'profitMargins': 0.117790006, 'enterpriseToEbitda': 10.796, '52WeekChange': -0.126822, 'morningStarRiskRating': None, 'forwardEps': 12.41, 'revenueQuarterlyGrowth': None, 'sharesOutstanding': 887891968, 'fundInceptionDate': None, 'annualReportExpenseRatio': None, 'bookValue': 22.524, 'sharesShort': 20805495, 'sharesPercentSharesOut': 0.0234, 'fundFamily': None, 'lastFiscalYearEnd': 1577750400, 'heldPercentInstitutions': 0.58555, 'netIncomeToCommon': 9018000384, 'trailingEps': 10.096, 'lastDividendValue': None, 'SandP52WeekChange': 0.039875627, 'priceToBook': 5.2393003, 'heldPercentInsiders': 0.0010800001, 'nextFiscalYearEnd': 1640908800, 'mostRecentQuarter': 1585612800, 'shortRatio': 4.37, 'sharesShortPreviousMonthDate': 1588204800, 'floatShares': 886506846, 'enterpriseValue': 173027524608, 'threeYearAverageReturn': None, 'lastSplitDate': 927763200, 'lastSplitFactor': '2:1', 'legalType': None, 'morningStarOverallRating': None, 'earningsQuarterlyGrowth': -0.261, 'dateShortInterest': 1590710400, 'pegRatio': 2.7, 'lastCapGain': None, 'shortPercentOfFloat': 0.0235, 'sharesShortPriorMonth': 19165408, 'category': None, 'fiveYearAverageReturn': None, 'regularMarketPrice': 126.03, 'logo_url': 'https://logo.clearbit.com/ibm.com'}

      Open
      High
      Low
      Close
      Volume
      Dividends
      Stock Splits

      Date

      1962-01-02
      0.62
      0.62
      0.61
      0.61
      387200
      0.0
      0.0

      1962-01-03
      0.61
      0.62
      0.61
      0.62
      288000
      0.0
      0.0

      1962-01-04
      0.62
      0.62
      0.61
      0.61
      256000
      0.0
      0.0

      1962-01-05
      0.61
      0.61
      0.60
      0.60
      363200
      0.0
      0.0

      1962-01-08
      0.60
      0.60
      0.58
      0.59
      544000
      0.0
      0.0

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...

      2020-06-05
      133.89
      134.23
      131.75
      132.06
      5892700
      0.0
      0.0

      2020-06-08
      132.27
      135.88
      132.27
      135.75
      5370500
      0.0
      0.0

      2020-06-09
      133.52
      133.60
      131.02
      131.87
      5393600
      0.0
      0.0

      2020-06-10
      132.15
      132.23
      129.80
      129.87
      4734800
      0.0
      0.0

      2020-06-11
      126.03
      126.10
      117.84
      118.01
      11754900
      0.0
      0.0

14712 rows × 7 columns

```python
aapl.actions
yf.pdr_override() # <== that's all it takes :-)

# download dataframe
data = pdr.get_data_yahoo("AAPL", start="2010-01-01", end="2020-06-11")
#? data
d=data.to_dict()
print(d.keys())
```

    [*********************100%***********************]  1 of 1 completed
    dict_keys(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'])

```python
dd=d["Close"]
```

```python
for key in dd.keys():
    new_key=(str(key).replace(' 00:00:00',''))
    dd[new_key] = dd.pop(key)
od= collections.OrderedDict(sorted(dd.items()))
```

```python
lists = sorted(dd.items()) # sorted by key, return a list of tuples
x, y = zip(*lists) # unpack a list of pairs into two tuples
plt.plot(x, y)
plt.show()
```

```python
#aapl.history(period="max")
```

```python
#data = pd.read_csv('APLS.csv')
#data.sample(frac=2, replace=True)
adj_close_price=y
dates=x
```

```python
adj_close_price=scale(adj_close_price)
#gets the price and dates from the matrix
#plt.plot(dates, adj_close_price)
```

```python
date_idx=[]
for i,j in enumerate(dates):
         date_idx.append(int(i))
```

```python
date_idx=np.array(date_idx[:],ndmin=2).T
adj_close_price=np.array(adj_close_price[:],ndmin=2).T
print(date_idx.shape)
print(adj_close_price.shape)
```

    (2628, 1)
    (2628, 1)

```python
#Number of neurons in the input, output, and hidden layers
inp = 1
output = 1
hidden = 20
#array of layers, 3 hidden and 1 output, along with the tanh activation function
layers = [('F', hidden), ('AF', 'tanh'), ('F', hidden), ('AF', 'tanh'), ('F', hidden), ('AF', 'relu'), ('F', output)]
#construct the model and dictate params
mlpr = ANNR([inp], layers, batchSize = 100, maxIter = 20000, tol = 0.2, reg = 1e-4, verbose = True)
```

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:211: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:212: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:222: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

    WARNING:tensorflow:
    The TensorFlow contrib module will not be included in TensorFlow 2.0.
    For more information, please see:
      * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
      * https://github.com/tensorflow/addons
      * https://github.com/tensorflow/io (for I/O related ops)
    If you depend on functionality not listed there, please file an issue.

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\tensorflow_core\contrib\layers\python\layers\layers.py:1634: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.
    Instructions for updating:
    Use keras.layers.flatten instead.
    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\tensorflow_core\python\layers\core.py:332: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
    Instructions for updating:
    Please use `layer.__call__` method instead.
    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:140: The name tf.squared_difference is deprecated. Please use tf.math.squared_difference instead.

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:152: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:153: The name tf.train.AdagradOptimizer is deprecated. Please use tf.compat.v1.train.AdagradOptimizer instead.

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:154: The name tf.train.FtrlOptimizer is deprecated. Please use tf.compat.v1.train.FtrlOptimizer instead.

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:155: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:265: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:267: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

    WARNING:tensorflow:From C:\Users\salmasi\.conda\envs\tensor2\lib\site-packages\TFANN.py:283: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

```python
holdDays = 5
totalDays = len(date_idx)
mlpr.fit(date_idx[0:(totalDays-holdDays)], adj_close_price[0:(totalDays-holdDays)])
```

    Iter     1	      6.68021047 (Batch Size:   100)
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```python
#Predict the stock price using the model
pricePredict = mlpr.predict(date_idx)
#Display the predicted reuslts agains the actual data
plt.plot(date_idx, adj_close_price)
plt.plot(date_idx, pricePredict, c='#5aa9ab')
plt.show()
```

```python
#Number of neurons in the input, output, and hidden layers
input2 = 1
output2 = 1
hidden2 = 50
#array of layers, 3 hidden and 1 output, along with the tanh activation function
layers = [('F', hidden2), ('AF', 'tanh'), ('F', hidden2), ('AF', 'tanh'), ('F', hidden2), ('AF', 'tanh'), ('F', output2)]
#construct the model and dictate params
mlpr2 = ANNR([input2], layers, batchSize = 256, maxIter = 10000, tol = 0.01, reg = 1e-4, verbose = True)
```

```python
holdDays = 5
totalDays = len(date_idx)
mlpr2.fit(date_idx[0:(totalDays-holdDays)], adj_close_price[0:(totalDays-holdDays)])
pricePredict2 = mlpr2.predict(date_idx)
```

    Iter     1	      3.04254016 (Batch Size:   256)
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    Iter  7260	      0.05845225 (Batch Size:   256)
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    Iter  7280	      0.06010227 (Batch Size:   256)
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    Iter  7300	      0.06157325 (Batch Size:   256)
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    Iter  7346	      0.07492030 (Batch Size:   256)
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    Iter  7587	      0.06527492 (Batch Size:   256)
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    Iter  7726	      0.05302197 (Batch Size:   256)
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    Iter 10000	      0.04124312 (Batch Size:   256)

```python
plt.plot(date_idx, adj_close_price)
plt.plot(date_idx, pricePredict, c='#5aa9ab')
plt.plot(date_idx, pricePredict2, c='#8B008B')
plt.show()
```

```python
pricePredict3 = mlpr2.predict(date_idx[-holdDays:])
print((pricePredict3-date_idx[-holdDays:])/date_idx[-holdDays:]*100)
```

    [[-99.89699759]
     [-99.89692712]
     [-99.89685796]
     [-99.89679009]
     [-99.89672359]]