gcp initial setup, python3 flask.

Author: Jason

README.md

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+# robinhood-ext
+This project is to implement some features that Robinhood lack of, for example, insightful/interactive visualization, news NLP analytics, trade execution/strategies. This is connected to Robinhood via an unofficial python APIs in this [repo](https://github.com/Jamonek/Robinhood).
+
+## Run Locally
+Go into the project directory, and start the FLask server by python.
+```
+python main.py
+```
+
+## Deploy to Google Cloud App Enginee
+The project is ready for GCP AppEng. You should install cloud SDK in your computer, then push by below command.
+```
+gcloud app deploy
+```

app.yaml

@@ -1,13 +1,17 @@
-runtime: python27
-api_version: 1
-threadsafe: true
+runtime: python
+env: flex
+entrypoint: gunicorn -b :$PORT main:app
 
-libraries:
-- name: ssl
-  version: latest
+runtime_config:
+  python_version: 3
 
-handlers:
-- url: /static
-  static_dir: static
-- url: /.*
-  script: server.app
+# This sample incurs costs to run on the App Engine flexible environment.
+# The settings below are to reduce costs during testing and are not appropriate
+# for production use. For more information, see:
+# https://cloud.google.com/appengine/docs/flexible/python/configuring-your-app-with-app-yaml
+manual_scaling:
+  instances: 1
+resources:
+  cpu: 1
+  memory_gb: 0.5
+  disk_size_gb: 10

dashboard.py

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+from Robinhood import Robinhood
+import pandas as pd
+from collections import defaultdict
+
+class InfoRetriever:
+    INTERESTED_STOCK_ATTR = [
+        'name', 'symbol', 'last_trade_price', 'quantity_held', 'dividend_yield', 'pe_ratio', 'pb_ratio', 
+        'open', 'high', 'low', 'volume', 'float', 'high_52_weeks', 'low_52_weeks', 'description', 
+        'sector', 'industry', 'market_cap'
+    ]
+    
+    def __init__(self, trader):
+        self.trader = trader
+    
+    def get_instruments_quant_owned(self):
+        securities_owned = self.trader.securities_owned()['results']
+        instruments_quant = []
+        for sec in securities_owned:
+            instrument_id, quantity = sec['instrument'].rsplit('/', 2)[-2], float(sec['quantity'])
+            instruments_quant.append((instrument_id, quantity))
+        return instruments_quant
+    
+    def get_stock_symbols_owned(self):
+        instruments_quant = self.get_instruments_quant_owned()
+        instrument_ids = []
+        for instrument_id, quant in instruments_quant:
+            instrument_ids.append(instrument_id)
+        return self.get_stock_symbols(instrument_ids)
+    
+    def get_stock_symbols(self, instruments):
+        symbols = []
+        for instrument in instruments:
+            symbols.append(self.get_stock_symbol(instrument))
+        return symbols
+    
+    def get_stock_symbol(self, instrument_id):
+        instrument_details = self.trader.instrument(instrument_id)
+        return (instrument_details['symbol'], instrument_details['simple_name'])
+    
+    def quotes(self, symbols):
+        return self.trader.quotes_data(symbols)
+    
+    def fundamentals(self, symbols):
+        fundamentals = []
+        for symbol in symbols:
+            fundamentals.append(self.trader.fundamentals(symbol))
+        return fundamentals
+    
+    def portfolio_holdings(self):
+        instruments_quant = self.get_instruments_quant_owned()
+        stocks_owned = self.get_stock_symbols_owned()
+        stock_symbols = [symbol for symbol, name in stocks_owned]
+        quotes = self.quotes(stock_symbols)
+        fundamentals = self.fundamentals(stock_symbols)
+        
+        df_rows = []
+        for (symbol, name), (instr, quant), quote, fundamental \
+            in zip(stocks_owned, instruments_quant, quotes, fundamentals):
+            merged_quote_fundamental_dict = quote.copy()
+            merged_quote_fundamental_dict.update(fundamental)
+            merged_quote_fundamental_dict['instrument_id'] = instr
+            merged_quote_fundamental_dict['quantity_held'] = quant
+            merged_quote_fundamental_dict['name'] = name
+            
+            df_row = {}
+            for attribute in InfoRetriever.INTERESTED_STOCK_ATTR:
+                df_row[attribute] = merged_quote_fundamental_dict[attribute]
+            df_rows.append(df_row)
+        
+        return pd.DataFrame(df_rows)
+
+def get_dashboard_data(user, password):
+    my_trader = Robinhood()
+    logged_in = my_trader.login(username=user, password=password)
+
+    retriever = InfoRetriever(my_trader)
+    portfolio_holdings = retriever.portfolio_holdings()
+
+    total_port_value = 0
+    sector_mkt_val = defaultdict(int)
+    for _, row in portfolio_holdings.iterrows():
+        total_port_value += float(row['last_trade_price']) * float(row['quantity_held'])
+        sector_mkt_val[row['sector']] += float(row['last_trade_price']) * float(row['quantity_held'])
+
+    sector_mkt_val_percent = {}
+    for sector, val in sector_mkt_val.items():
+        sector_mkt_val_percent[sector] = val / total_port_value * 100
+
+    data = []
+    for _, row in portfolio_holdings.iterrows():
+        stock_info = {
+            'name': row['name'],
+            'symbol': row['symbol'],
+            'market_cap': float(row['market_cap']),
+            'pe_ratio': -6 if row['pe_ratio'] is None or float(row['pe_ratio']) < 0 else float(row['pe_ratio']),
+            'dividend_yield': 0 if row['dividend_yield'] is None else float(row['dividend_yield']),
+            'holding_val': float(row['last_trade_price']) * float(row['quantity_held']),
+            'last_trade_price': float(row['last_trade_price']),
+            'quantity_held': float(row['quantity_held']),
+            'port_percent': float(row['last_trade_price']) * float(row['quantity_held']) / total_port_value * 100,
+            'industry': row['industry'],
+            'sector': row['sector'],
+            'sector_port_percent': sector_mkt_val_percent[row['sector']],
+            'description': row['description'],
+        }
+        data.append(stock_info)
+
+    return data

main.py

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+from flask import Flask, render_template, request, jsonify
+from dashboard import get_dashboard_data
+
+app = Flask(__name__)
+
+# Dashboard
+@app.route('/api/data/dashboard', methods=["POST"])
+def api_dashboard_data():
+    username = request.form['username']
+    password = request.form['password']  
+    return jsonify(get_dashboard_data(username, password))
+
+@app.route('/', methods=['GET'])
+def dashboard():
+    return render_template('dashboard.html')
+
+
+if __name__ == '__main__':
+    # This is used when running locally. Gunicorn is used to run the
+    # application on Google App Engine. See entrypoint in app.yaml.
+    app.run(host='127.0.0.1', port=8080, debug=True)

requirements.txt

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-Flask==0.12.4
-Werkzeug<0.13.0,>=0.12.0
+Flask==0.12.2
+gunicorn==19.7.1
+six
+requests
+python-dateutil
+pandas
+numpy

server.py

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+    .chart rect {
+        fill: steelblue;
+    }
+    .chart rect:hover {
+        fill: turquoise;
+    }
+    .chart .rectM {
+        stroke: green;
+        stroke-width: 1;
+        fill: green;
+        fill-opacity: .2;
+    }
+    .chart .rectM:hover {
+        fill: green;
+        fill-opacity: .5;
+    }
+    .chart text {
+        font: 10px sans-serif;
+    }
+    .chart .title {
+        font: 15px sans-serif;
+    }
+    .axis path,
+    .axis line {
+    fill: none;
+    stroke: #000;
+    shape-rendering: crispEdges;
+    }
+    .chart .tooltip {
+        display: inline-block;
+        text-align: center;
+        color: white;
+        background-color: darkslategray;
+        padding: 5px 5px 5px 5px;
+    }
+    .tooltip .title {
+        font-weight: bold;
+    }
+    #marker {
+       fill: red;
+       font-weight: bold;
+       font: 13px sans-serif;
+    }