trading system script provided

Author: uberdeveloper

tradingsystem/trading_system.py

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+"""
+Trading system as a script.
+See the instructions in the notebook on how this works.
+The only change here is that the bhav file is
+automatically picked up from the web. You need to
+change this manually in the utils.py file if there is a holiday.
+"""
+
+# Import libraries
+import pandas as pd
+import os
+import datetime
+import json
+from jinja2 import Environment, FileSystemLoader
+from utils import *
+
+# PARAMETERS
+# Only change the parameters to 
+
+UNIVERSE = 'NIFTY50' # Universe to be searched
+STOP_LOSS = 3 # Stop loss for the order
+NUM_STOCKS = 5 # Number of stocks to sell
+CAPITAL = 20000
+LEVERAGE = 1
+ORDERFILE_PREFIX = 'orders_' # Prefix file name to store
+DIVERSIFY = False # whether to diversify data among sectors
+ACCOUNTID = 'XXXXXX' # For any user
+APIKEY = 'xxxxxxxxxxxxxxxx' # For zerodha
+
+# Prepare the dataframe
+yesterday, today = pd.bdate_range(end=datetime.datetime.now(), periods=2)
+preopen = fetch_preopen_data()
+eod = get_bhav_copy(yesterday)
+symbols = pd.read_excel('universe.xlsx', sheet_name=UNIVERSE, header=None).values.ravel()
+sectors = pd.read_csv('sectors.csv')
+df = eod[eod['SYMBOL'].isin(symbols)]
+df = df[df['SERIES'] == "EQ"].reset_index(drop=True)
+df = df.merge(sectors)
+
+
+# Diversify function for diversifying sectors
+def diversify(frame, n=5):
+    """
+    Diversify stocks among sectors
+    frame
+        dataframe
+    n
+        number of stocks
+    """
+    industry = set()
+    stocks = []
+    for k,v in frame.iterrows():
+        if v.at['Industry'] not in industry:
+            industry.add(v.at['Industry'])
+            stocks.append(v)
+            if len(stocks) == n:
+                break
+    f = pd.DataFrame(stocks)
+    if len(f) == 0:
+        return pd.DataFrame()
+    else:
+        return f        
+
+# Trading logic
+
+df['RET'] = (df['CLOSE']/df['PREVCLOSE']) - 1
+if DIVERSIFY:
+    result = diversify(df.sort_values(by='RET', ascending=False), NUM_STOCKS)
+else:
+    result = df.sort_values(by='RET', ascending = False).iloc[:NUM_STOCKS]
+trading_capital = CAPITAL * LEVERAGE
+orders = result.merge(preopen, on='SYMBOL')
+num_stock = len(orders)
+
+# Pricing logic goes here
+
+orders['trigger_price'] = (orders['OpenPrice'] - 0.05).round(2)
+orders['price'] = (orders['trigger_price'] - 0.05).round(2)
+
+# I prefer a constant percentage change instead of value
+# If you prefer it, uncomment the below two lines
+
+#orders['trigger_price'] = orders['OpenPrice'] * 0.9985
+#orders['price'] = orders['trigger_price'] - 0.05
+
+orders['stop_loss'] = (orders['price'] * (1 + STOP_LOSS * 0.01)).apply(tick).round(2)
+orders['qty'] = (trading_capital/num_stock/orders['price']).astype(int)
+orders['order'] = 'SELL'
+filename = ORDERFILE_PREFIX + datetime.datetime.today().strftime('%Y-%m-%d') + '.csv'
+orders.to_csv('orders/' + filename,  index=False)
+
+def generate_nest():
+    """
+    Most brokers and software provide an option to place basket orders. 
+    So we are going to create a basket order from our orders.  We would be using the ``create_order`` function from ``utils.py`` file.
+    To do this for your specific broker, do the following steps
+        1. Know the format of your broker; you can do this by placing a basket order and exporting it
+        2. The format would usually have a list of columns to be filled up. We need to fill all the columns to import our order.
+        3. We separate the columns into columns that are already in our dataframe and columns to be included
+        4. We prepare a list of matching columns and rename them
+        5. For new columns, we create a python dictionary with keys as column names and values as the value for the column (we assume that these columns have a single value)
+
+    Thanks @vjay for providing the necessary support
+    """
+    # List of columns to be included in the output
+
+    cols = [
+        'Segment', 'InstrumentName', 'Symbol', 'Option Type', 'Strike Price',
+        'ExpiryDate', 'Price', 'Qty', 'LTP', 'Buy/Sell', 'Order Type',
+        'TriggerPrice', 'Pro/Cli', 'P Type', 'Validity', 'AccountId',
+        'Validity Date', 'Remarks', 'Participant code', 'Validity Time',
+        'Market Proc'    
+    ]
+
+    # These columns are common for all orders - columns with a single name
+    columns = {
+        'Segment': 'NSE',
+        'InstrumentName': 'EQ',
+        'Option Type': 'NA',
+        'Strike Price': 'NA',
+        'ExpiryDate': 'NA',
+        'LTP': 0,
+        'Disclosed Qty': 0,
+        'AccountId': ACCOUNTID,
+        'Pro/Cli': 'CLI',
+        'Validity': 'DAY',
+        'P Type': 'MIS',
+        'Remarks': '',
+        'Validity Date': 'NA',
+        'Participant code': '',
+        'Validity Time': 'NA',
+        'Market Proc': 'NA',
+        'Order Type': 'SL'
+    }
+
+    # These are columns to be renamed
+    rename = {
+        'order': 'Buy/Sell',
+        'price': 'Price',
+        'qty': 'Qty',
+        'trigger_price': 'TriggerPrice',
+        'price': 'Price' ,
+        'SYMBOL': 'Symbol'
+    }
+
+
+    # Generating orders in the required format
+    entry_orders = orders.copy()
+    entry = create_orders(entry_orders, rename=rename, **columns)
+
+    # Exit orders order type and price to be changed
+    exit_orders = orders.copy()
+    exit_orders['order'] = 'BUY'
+    exit_orders['price'] = 0
+    exit_orders['trigger_price'] = stop_loss(orders['price'], 3, order='S').round(2)
+    columns.update({'Order Type': 'SL-M'})
+    exit = create_orders(exit_orders, rename=rename, **columns)
+
+    # File generation
+    entry.append(exit, sort=False)[cols].to_csv('orders_to_place.csv', 
+                       index=False, header=False)
+
+    print('File generated for NEST')
+
+def generate_zerodha():
+    """
+    Order generation for Kite Zerodha
+        1. Sign up for a zerodha publisher api key at(https://kite.trade/)
+        2. Update the API key in parameters
+        3. This would create an html file **zerodha_order.html** in your present working directory.
+        4. Open the HTML file and click the submit button to log into zerodha and place your orders.
+        5. Check the updated at time to make sure that this is the latest generated order.
+    """
+
+    # List of columns to be included in the output
+    cols = [
+       'tradingsymbol', 'exchange', 'transaction_type', 'order_type',
+        'quantity', 'product', 'validity', 'price', 'trigger_price'
+    ]
+
+    # These columns are common for all orders - columns with a single name
+    columns = {
+        'exchange': 'NSE',
+        'product': 'MIS',
+        'validity': 'DAY',
+        'order_type': 'SL'
+    }
+
+    # These are columns to be renamed
+    rename = {
+        'order': 'transaction_type',
+        'price': 'Price',
+        'qty': 'quantity',
+        'trigger_price': 'trigger_price',
+        'price': 'price' ,
+        'SYMBOL': 'tradingsymbol'
+    }
+
+    # Generating orders in the required format
+    entry_orders = orders.copy()
+    entry = create_orders(entry_orders, rename=rename, **columns)
+
+    # Exit orders order type and price to be changed
+    exit_orders = orders.copy()
+    exit_orders['order'] = 'BUY'
+    exit_orders['price'] = 0
+    exit_orders['trigger_price'] = stop_loss(orders['price'], 3, order='S').round(2)
+    columns.update({'order_type': 'SL-M'})
+    exit = create_orders(exit_orders, rename=rename, **columns)
+    trades = entry.append(exit, sort=False)[cols].to_dict(orient='records')
+
+    # Order HTML file generated
+    env = Environment(loader=FileSystemLoader('.'))
+    template = env.get_template('template.html')
+    output_from_parsed_template = template.render(api_key = APIKEY,
+                                                  orders=json.dumps(trades), 
+                                                  date=str(datetime.datetime.now()))
+
+    with open('zerodha_order.html', 'w') as f:
+        f.write(output_from_parsed_template)
+        print('Zerodha order file generated')
+
+if __name__ == "__main__":
+    generate_nest()
+    generate_zerodha()
+
+