Test Cache

.gitignore

@@ -1 +1,4 @@
-dist/
+dist/
+MANIFEST
+index.py
+stock_wrapper/__cache__/

Examples/example.py

@@ -1,25 +1,18 @@
 import stock_wrapper
 from stock_wrapper import Stock
 
+# getting the most updated prices requires a FREE robinhood account
+stock_wrapper.robin.login('email', 'password')
+
 apple = Stock('AAPL')
 microsoft = Stock('MSFT')
 
 apple.price # get the current price
 microsoft.price
 
-apple.history # get the entire history of the stock including Moving Averages
-microsoft.history
-
+# data analysis does NOT require a robinhood account
 stock_wrapper.visualize.graph_trendline_analysis([apple, microsoft])
 stock_wrapper.visualize.graph_candlestick_analysis([apple, microsoft])
 
-portfolio = stock_wrapper.robin.build_portfolio()
-portfolio.append(Stock('LYFT'))
-portfolio.append(Stock('UBER'))
-portfolio.append(Stock('AMD'))
-portfolio.append(Stock('PYPL'))
-portfolio.append(Stock('CSCO'))
-# stock_wrapper.visualize.graph_trendline_analysis(portfolio)
-# stock_wrapper.visualize.graph_candlestick_analysis(portfolio)
+stock_wrapper.visualize.graph_candlestick_analysis([Stock('TSLA')])
 
-stock_wrapper.visualize.graph_candlestick_analysis([Stock('SUN')])

stock_wrapper/data.py

@@ -3,8 +3,10 @@
 import yfinance
 import robin_stocks
 import pandas as pd
+import os
 import numpy as np
 
+import json
 import datetime
 
 class data:
@@ -17,35 +19,36 @@ class data:
         'max': 'max'
     }
 
-    def __init__(self):
-        pass
-
     @classmethod
-    def get_history(cls, ticker_symbol, calculate_averages=False):
+    def get_history(cls, ticker_symbol, calculate_averages=True, cache=True):
         """Takes in a ticker object and returns a pandas dataframe containing price,
         :param stock: single Ticker Symbol
         :type: str
         :return: [list]: (timeframe <datetime.datetime>, price <int>)
         """
+        if cache and cls.cache.exists(ticker_symbol):
+            return pd.read_json(cls.cache.read_file(ticker_symbol))
+        else:
+            history = yfinance.Ticker(ticker_symbol).history(period='max').reset_index()
+            history['Average'] = (history['High'] + history['Low']) / 2
 
-        history = yfinance.Ticker(ticker_symbol).history(period='max').reset_index()
-        history['Average'] = (history['High'] + history['Low']) / 2
+            def __build_average(df, period):
+                name = (str(period) + '_SMA')
 
-        def __build_average(df, period):
-            name = (str(period) + '_SMA')
+                if len(history) > period:
+                    history.loc[0:period, name] = 0
 
-            if len(history) > period:
-                history.loc[0:period, name] = 0
+                    index = period + 1
+                    while index < len(history):
+                        history.iloc[index, history.columns.get_loc(name)] = history.iloc[index - period:index]['Close'].sum() / period
+                        index += 1
 
-                index = period + 1
-                while index < len(history):
-                    history.iloc[index, history.columns.get_loc(name)] = history.iloc[index - period:index]['Close'].sum() / period
-                    index += 1
+            if calculate_averages:
+                __build_average(history, 50)
+                __build_average(history, 100)
+                __build_average(history, 200)
 
-        if calculate_averages:
-            __build_average(history, 50)
-            __build_average(history, 100)
-            __build_average(history, 200)
+            cls.cache.write_file(ticker_symbol, history.to_json())
 
         return history
 
@@ -71,3 +74,33 @@ def get_historical_prices(cls, ticker_symbol, span='day'):
     @staticmethod
     def __get_time(time, conversion=-5):
         return datetime.datetime.strptime(time, "%Y-%m-%dT%H:%M:%SZ") + datetime.timedelta(hours=conversion)
+
+    @classmethod
+    def clear_cache(cls):
+        cls.cache.clear()
+
+    class cache:
+        @staticmethod
+        def read_file(ticker_name):
+            path = os.path.abspath('stock_wrapper/__cache__/' + ticker_name + '.py')
+            with open(path) as json_file:
+                data = json.load(json_file)
+
+            return data
+
+        @staticmethod
+        def write_file(ticker_name, data):
+            path = os.path.abspath('stock_wrapper/__cache__/' + ticker_name + '.py')
+            with open(path, 'w+') as outfile:
+                json.dump(data, outfile)
+
+        @staticmethod
+        def exists(ticker_name):
+            path = os.path.abspath('stock_wrapper/__cache__/' + ticker_name + '.py')
+            return os.path.exists(path)
+
+        @staticmethod
+        def clear():
+            dirpath = os.path.abspath('stock_wrapper/__cache__/')
+            for path in os.listdir(dirpath):
+                os.remove(os.path.abspath('stock_wrapper/__cache__/' + path))

stock_wrapper/visualize.py

@@ -9,10 +9,8 @@
 from plotly.subplots import make_subplots
 
 import threading
-import multiprocessing as mp
 import curses
 import time
-import datetime
 import numpy as np
 
 
@@ -155,7 +153,7 @@ def __graph(stock):
         plt.show()
 
     @staticmethod
-    def graph_candlestick_analysis(stocks):
+    def graph_candlestick_analysis(stocks, cache=True):
         """Takes in a list of Ticker Symbols and optional span. Displays a matplot graph with the history of that stock, default span to one day
         :param stock: list of Stock objects
         :type stock: list [<stock_wrapper.Stock>]
@@ -167,7 +165,7 @@ def __graph(stock, data):
             sns.set(style="darkgrid")
 
             fig = make_subplots(specs=[[{"secondary_y": True}]])
-            fig.add_trace(go.Candlestick(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='Market Price'))
+            fig.add_trace(go.Candlestick(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name=stock.ticker + 'Market Price'))
             fig.add_trace(go.Scatter(x=data['Date'], y=data['50_SMA'], name='50 Day Moving Average', marker_color='rgba(13, 140, 214, .8)'))
             fig.add_trace(go.Scatter(x=data['Date'], y=data['100_SMA'], name='100 Day Moving Average', marker_color='rgba(230, 223, 23, .8)'))
             fig.add_trace(go.Scatter(x=data['Date'], y=data['200_SMA'], name='200 Day Moving Average', marker_color='rgba(255, 165, 0, .8)'))
@@ -177,13 +175,13 @@ def __graph(stock, data):
 
         data = []
         for stock in stocks:
-            data.append(stock_wrapper.data.get_history(stock.ticker, calculate_averages=True))
+            data.append(stock_wrapper.data.get_history(stock.ticker, calculate_averages=True, cache=cache))
 
         for i in range(len(data)):
             __graph(stocks[i], data[i])
 
     @staticmethod
-    def graph_trendline_analysis(stocks):
+    def graph_trendline_analysis(stocks, cache):
         """Takes in a list of Ticker Symbols and optional span. Displays a matplot graph with the history of that stock, default span to one day
         :param stock: list of Stock objects
         :type stock: list [<stock_wrapper.Stock>]
@@ -214,7 +212,7 @@ def __graph(stock, data):
 
         data = []
         for stock in stocks:
-            data.append(stock_wrapper.data.get_history(stock.ticker, calculate_averages=True))
+            data.append(stock_wrapper.data.get_history(stock.ticker, calculate_averages=True, cache=cache))
 
         for i in range(len(data)):
             __graph(stocks[i], data[i])