commit

Author: kloudtrader-github

back.ipynb

@@ -0,0 +1,81 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Tuesday, October 01, 2019 03:17:53 PM IST INFO: Starting Backtest for ba from 2019-09-24 09:30:00 to 2019-09-24 15:30:00 with initial capital = 100000\n",
+      "Tuesday, October 01, 2019 03:17:56 PM IST INFO: Received Signals from ba\n"
+     ]
+    }
+   ],
+   "source": [
+    "from libkloudtrader.algorithm import *\n",
+    "import libkloudtrader.analysis as analysis\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "\n",
+    "def ba(backtest,data):\n",
+    "    '''\n",
+    "    data['high']=data.close.shift(1).rolling(window=5).max()\n",
+    "    data['low']=data.close.shift(1).rolling(window=5).min()\n",
+    "    data['avg']=analysis.ma(data.close,5)\n",
+    "    buy=(data.close>data.high)\n",
+    "    sell=(data.close<data.avg)\n",
+    "    #short=(data.close<data.low)\n",
+    "    #cover=(data.close>data.avg)\n",
+    "    if buy.tail(1).bool():\n",
+    "        backtest.buy(5)\n",
+    "    elif sell.tail(1).bool():\n",
+    "        backtest.sell(5)\n",
+    "\n",
+    "    '''\n",
+    "    pass\n",
+    "    #rets=analysis.daily_returns(data.close)\n",
+    "    #data['volatility']=analysis.moving_volatility(rets,1)\n",
+    "    #data['slip']=data.high-data.close\n",
+    "   \n",
+    "\n",
+    "\n",
+    "    \n",
+    "\n",
+    "run_backtest(ba,['AAPL'],data=\"US_STOCKS_ohlcv\",start='2019-09-24 09:30:00',end='2019-09-24 15:30:00',data_interval='15m')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

back.py

@@ -6,7 +6,7 @@
 
 
 def ba(backtest,data):
-    '''
+
     data['high']=data.close.shift(1).rolling(window=5).max()
     data['low']=data.close.shift(1).rolling(window=5).min()
     data['avg']=analysis.ma(data.close,5)
@@ -19,8 +19,7 @@ def ba(backtest,data):
     elif sell.tail(1).bool():
         backtest.sell(5)
 
-    '''
-    pass
+
     #rets=analysis.daily_returns(data.close)
     #data['volatility']=analysis.moving_volatility(rets,1)
     #data['slip']=data.high-data.close
@@ -29,4 +28,4 @@ def ba(backtest,data):
 
 
 
-run_backtest(ba,['AAPL'],data="US_STOCKS_ohlcv",start_date='2019-01-01',end_date='2019-07-01')
+run_backtest(ba,['AAPL'],data="US_STOCKS_times_and_sale",start='2019-09-24 09:30:00',end='2019-09-24 15:30:00',data_interval='15m')

libkloudtrader/algorithm.py

@@ -26,26 +26,25 @@
 def run_backtest(strategy: str,
              symbol_bucket: List[str],
              data: str,
-             start_date: Any,
-             end_date: Any,
+             start: Any,
+             end: Any,
+             data_interval:str="1d",
              preferred_price_point: str = 'close',
              preferred_benchmark: str = 'SPY',
-             data_interval: str = '1d',
              initial_capital: float = 100000,
              commission: float = 0,
              slippage=True):
     """Backtester function"""
     try:
         logger.info(
             'Starting Backtest for {} from {} to {} with initial capital = {}'.
-            format(strategy.__name__, start_date, end_date, initial_capital))
+            format(strategy.__name__, start, end, initial_capital))
         data_to_backtest_on = Data_Types[data].value
         for symbol in symbol_bucket:
-            data_batch = data_to_backtest_on(symbol,
-                                             start_date,
-                                             end_date
+            data_batch = data_to_backtest_on(symbol=symbol,
+                                             start=start,
+                                             end=end,interval=data_interval
                                              )
-            print(data_batch)
             batch = processing.Buffer(len(data_batch), dtype=object)
             backtest=bt.Backtest(capital=100000,commission=1,enable_slippage=True)
             for datetime, bar in data_batch.iterrows():
@@ -54,14 +53,16 @@ def run_backtest(strategy: str,
                 data_batch = pd.DataFrame(batch)
 
                 locals()['strategy'](backtest,data_batch)
-            #print(pd.merge(data_batch,backtest.get_portfolio,on = index))
-            #print(data_batch)
+            
+            print(backtest.get_trade_log)
+            del backtest
+            
 
         '''
         for symbol in symbol_bucket:
             data_batch = data_to_backtest_on(symbol,
-                                             start_date,
-                                             end_date,
+                                             start,
+                                             end,
                                              interval=data_interval)
             for symbol in symbol_bucket:
 
@@ -88,7 +89,7 @@ def run_backtest(strategy: str,
                 #df['positions in '+symbol]=100*df['positions']
                 #print(bt.trades)
 
-        logger.info("Received Signals from {}".format(strategy.__name__))
+        #logger.info("Received Signals from {}".format(strategy.__name__))
 
     except (KeyboardInterrupt, SystemExit):
         print('\n')
@@ -102,8 +103,8 @@ def run_backtest(strategy: str,
         raise exception
         exit()
 
-    #print(return_data_from_enum(a,symbol,start_date, end_date))
-    #print(locals()[a](symbol, start_date, end_date))
+    #print(return_data_from_enum(a,symbol,start, end))
+    #print(locals()[a](symbol, start, end))
 
 
 def run_live(strategy: str,

libkloudtrader/backtest.py

@@ -44,27 +44,28 @@ def __init__(self,capital:float,commission:float,enable_slippage:bool):
         self.position=0
         self.commission=commission
         self.enable_slippage=enable_slippage
-        self.slippage=0
+        self.slippage=0.0
+        self.asset_price=0.0
         self.trades_log=pd.DataFrame(columns=['datetime','trade_type','price','fill_price','order_cost','capital','position'])
 
 
     def buy(self,quantity):
         '''emulates a buy order'''
         self.capital-=self.order_cost
         self.update_positions(quantity)
-        self.update_trade_logs(datetime=self.bar.datetime,trade_type='Buy',price=self.bar.close,fill_price=self.fill_price,order_cost=self.order_cost,capital=self.capital,position=self.position)
+        self.update_trade_logs(datetime=self.bar.datetime,trade_type='Buy',price=self.asset_price,fill_price=self.fill_price,order_cost=self.order_cost,capital=self.capital,position=self.position)
         #print('Bought {} of stocks @ {} but price is {}'.format(quantity,self.order_cost,self.bar.close))
 
 
     def sell(self,quantity):
         '''emulates a sell order'''
-        #if self.position!=0:
-        self.capital+=self.order_cost
-        self.update_positions(-1*quantity)
-        self.update_trade_logs(datetime=self.bar.datetime,trade_type='Sell',price=self.bar.close,fill_price=self.fill_price,order_cost=self.order_cost,capital=self.capital,position=self.position)
+        if self.position!=0:
+            self.capital+=self.order_cost
+            self.update_positions(-1*quantity)
+            self.update_trade_logs(datetime=self.bar.datetime,trade_type='Sell',price=self.asset_price,fill_price=self.fill_price,order_cost=self.order_cost,capital=self.capital,position=self.position)
         #print('Sold {} of stocks @ {} but price is {}'.format(quantity,self.order_cost,self.bar.close))
-        #else:
-            #pass#print('No position to close')
+        else:
+            print('No position to close')
 
     def update_trade_logs(self,datetime,trade_type,price,fill_price,order_cost,capital,position):
         df=pd.DataFrame([{'datetime':datetime,'trade_type':trade_type,'price':price,'fill_price':fill_price,'order_cost':order_cost,'capital':capital,'position':position}])
@@ -79,29 +80,30 @@ def get_trade_log(self):
     def update_bar(self,index,bar):
         self.bar=bar
         self.bar.datetime=index
+        if 'price' in self.bar:
+            self.asset_price=self.bar.price
+        else:
+            self.asset_price=self.bar.close
 
     def update_positions(self,quantity):
         self.position+=quantity
 
     @property
     def calculate_commission(self):
-        commiss=(self.commission/100)*self.bar.close
-        return commiss
+        return (self.commission/100)*self.fill_price
+         
 
     @property
     def calculate_slippage(self):
         '''how is slippage calculated?'''
         '''slippage should not be more than 2% in most of the trades. so we generate a random percentage b/w 0-2.use that number as %age'''
         if self.enable_slippage:
-            slippage_percent= np.random.uniform(low=0, high=0.02)
-            slippage=slippage_percent*self.bar.close
-            return slippage
+            return np.random.uniform(low=0, high=0.02)*self.asset_price
         return 0.0
 
     @property   
     def fill_price(self):
-        return self.bar.close+self.calculate_slippage
-        
+        return self.asset_price+self.calculate_slippage        
 
     @property   
     def order_cost(self):

libkloudtrader/enumerables.py

@@ -8,6 +8,8 @@
 class Data_Types(Enum):
     '''Data Types of backtesting and live trading'''
     US_STOCKS_ohlcv = partial(stocks.ohlcv)
+    US_STOCKS_times_and_sale=partial(stocks.time_and_sale)
+    CRYPTO_ohlcv=partial(crypto.ohlcv)
     US_STOCKS_live_feed = partial(stocks.incoming_tick_data_handler)
     CRYPTO_live_feed = partial(crypto.incoming_tick_data_handler)
     CRYPTO_live_feed_level2 = partial(crypto.incoming_tick_data_handler_level2)

libkloudtrader/stocks.py

@@ -493,41 +493,17 @@ def ohlcv(symbol: str,
     else:
         logger.error('Oops! An error Occurred ⚠️')
         raise InvalidBrokerage
-    if interval not in ("1d", "1w", "1M", "1m", "5m", "15m"):
+    if interval not in ("1d", "1w", "1M"):
         logger.error('Oops! An error Occurred ⚠️')
         raise InvlaidTimeInterval(
-            'Invalid Time Interval. Available time intervals for this function are: "1d","1w","1M","1m","5m","15m" '
+            'Invalid Time Interval. Available time intervals for this function are: "1d","1w","1M"'
         )
     if interval == "1d":
         interval = "daily"
     elif interval == "1w":
         interval = "weekly"
     elif interval == "1M":
         interval = "monthly"
-    elif interval == "1m":
-        return min1_bar_data(symbol=symbol,
-                             start=start,
-                             end=end,
-                             data_filter='open',
-                             brokerage=brokerage,
-                             access_token=access_token,
-                             dataframe=True)
-    elif interval == "5m":
-        return min5_bar_data(symbol=symbol,
-                             start=start,
-                             end=end,
-                             data_filter='open',
-                             brokerage=brokerage,
-                             access_token=access_token,
-                             dataframe=True)
-    elif interval == "15m":
-        return min15_bar_data(symbol=symbol,
-                              start=start,
-                              end=end,
-                              data_filter='open',
-                              brokerage=brokerage,
-                              access_token=access_token,
-                              dataframe=True)
     params: dict = {
         "symbol": str(symbol.upper()),
         "start": str(start),
@@ -558,6 +534,59 @@ def ohlcv(symbol: str,
         logger.error('Oops! An error Occurred ⚠️')
         raise InvalidCredentials(response.text)
 
+def time_and_sale(symbol: str,
+          start: str,
+          end: str,
+          interval: str,
+          data_filter: str = "all",
+          brokerage: typing.Any = USER_BROKERAGE,
+          access_token: str = USER_ACCESS_TOKEN,
+          dataframe: bool = True)->dict:
+    '''get time and sale data'''
+    if brokerage == "Tradier Inc.":
+        url = TR_BROKERAGE_API_URL
+    elif brokerage == "miscpaper":
+        url = TR_SANDBOX_BROKERAGE_API_URL
+    else:
+        logger.error('Oops! An error Occurred ⚠️')
+        raise InvalidBrokerage
+    
+    if interval not in ("1m", "5m", "15m"):
+        logger.error('Oops! An error Occurred ⚠️')
+        raise InvlaidTimeInterval(
+            'Invalid Time Interval. Available time intervals for this function are: "1m", "5m", "15m"'
+        )
+    interval_dict={'1m':'1min','5m':'5min','15m':'15min'}
+    params = {
+        "symbol": str.upper(symbol),
+        "interval": interval_dict[interval],
+        "start": start,
+        "end": end,
+        "session_filter": str(data_filter),
+    }
+    response = requests.get(
+        "{}/v1/markets/timesales".format(url),
+        headers=tr_get_headers(access_token),
+        params=params,
+    )
+    if response:
+        if not dataframe:
+            return response.json()
+        else:
+            data = response.json()["series"]["data"]
+            dataframe = pandas.DataFrame(data)
+            dataframe['datetime'] = pandas.to_datetime(dataframe['time'])
+            dataframe.set_index(['datetime'], inplace=True)
+            del dataframe['time']
+            del dataframe['timestamp']
+            return dataframe
+    if response.status_code == 400:
+        logger.error('Oops! An error Occurred ⚠️')
+        raise BadRequest(response.text)
+    if response.status_code == 401:
+        logger.error('Oops! An error Occurred ⚠️')
+        raise InvalidCredentials(response.text)
+
 
 def stream_live_quotes(symbol: str,
                        brokerage: str = USER_BROKERAGE,

setup.py

@@ -8,7 +8,7 @@
     packages=['libkloudtrader'],
     url='https://github.com/KloudTrader/kloudtrader',
     license='LICENSE',
-    description="kloudTrader's in-house library that makes it much easier for you to code algorithms that can trade for you.",
+    description="KloudTrader's in-house library that makes it much easier for you to code algorithms that can trade for you.",
     long_description_content_type="text/markdown",
     long_description='pypi.md',
     install_requires=[