diff --git a/examples/Binaries.ipynb b/examples/Binaries.ipynb
new file mode 100644
index 0000000..dc2d7fd
--- /dev/null
+++ b/examples/Binaries.ipynb
@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/nhaga/dev/projects/opstrat\n"
+     ]
+    }
+   ],
+   "source": [
+    "import opstrat as op"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "op.single_plotter(\n",
+    "    op_type='bp',\n",
+    "    spot=100,\n",
+    "    spot_range=20,\n",
+    "    strike=105,\n",
+    "    tr_type='b',\n",
+    "    op_pr=0.1,\n",
+    "    contracts=1\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "op1={'op_type':'c','strike':100,'tr_type':'b','op_pr':2,'contract':1}\n",
+    "op2={'op_type':'c','strike':110,'tr_type':'s','op_pr':1,'contract':1}\n",
+    "op3={'op_type':'bc','strike':110,'tr_type':'s','op_pr':0.001,'contract':10}\n",
+    "\n",
+    "op.multi_plotter(spot_range=20, spot=100, op_list=[op1,op2,op3])\n",
+    "    "
+   ]
+  },
+  {
+   "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": "3.10.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/opstrat/basic_multi.py b/opstrat/basic_multi.py
index 2630276..51bfe19 100644
--- a/opstrat/basic_multi.py
+++ b/opstrat/basic_multi.py
@@ -1,12 +1,14 @@
 #multiplotter
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 import seaborn as sns
 
-from .helpers import payoff_calculator, check_optype, check_trtype
+from .helpers import check_optype, check_trtype, payoff_calculator
 
 abb={'c': 'Call',
     'p': 'Put',
+    'bc': 'Binary Call',
+    'bp': 'Binary Put',
     'b': 'Long',
     's': 'Short'}
 
@@ -34,8 +36,8 @@ def multi_plotter(spot_range=20, spot=100,
           Transaction Type>> 'b': long, 's': short
        'op_pr': int, float, default: 10
           Option Price
-       'op_type': kind {'c','p'}, default:'c'
-          Opion type>> 'c': call option, 'p':put option
+        'op_type': kind {'c','p', 'bc', 'bp'}, default:'c'
+            Opion type>> 'c': call option, 'p':put option, 'bc':binary call, 'bp': binary put 
        'contracts': int default:1, optional
            Number of contracts
     
diff --git a/opstrat/basic_single.py b/opstrat/basic_single.py
index 97067a9..8f16012 100644
--- a/opstrat/basic_single.py
+++ b/opstrat/basic_single.py
@@ -1,23 +1,27 @@
-import numpy as np
+import warnings
+
 import matplotlib.pyplot as plt
+import numpy as np
 import seaborn as sns
-import warnings 
-from .helpers import check_optype, check_trtype
+
+from .helpers import check_optype, check_trtype, payoff_calculator
 
 warnings.filterwarnings('ignore')
 
 abb={'c': 'Call',
     'p': 'Put',
+    'bc': 'Binary Call',
+    'bp': 'Binary Put',
     'b': 'Long',
     's': 'Short'}
 
-def single_plotter(op_type='c',spot=100, spot_range=10,strike=102,tr_type='b',op_pr=2, save=False, file='fig.png'):
+def single_plotter(op_type='c',spot=100, spot_range=10,strike=102,tr_type='b',op_pr=2, contracts=1, save=False, file='fig.png'):
     """
     Plots a basic option payoff diagram for a single option
     Parameters
     ----------
-    op_type: kind {'c','p'}, default:'c'
-       Opion type>> 'c': call option, 'p':put option 
+    op_type: kind {'c','p', 'bc', 'bp'}, default:'c'
+       Opion type>> 'c': call option, 'p':put option, 'bc':binary call, 'bp': binary put 
     
     spot: int, float, default: 100 
        Spot Price
@@ -33,6 +37,10 @@ def single_plotter(op_type='c',spot=100, spot_range=10,strike=102,tr_type='b',op
 
     op_pr: int, float, default: 10
     Option Price
+
+    contracts: int default:1, optional
+    Number of contracts
+
     
     save: Boolean, default False
         Save figure
@@ -54,22 +62,11 @@ def single_plotter(op_type='c',spot=100, spot_range=10,strike=102,tr_type='b',op
     check_optype(op_type)
     check_trtype(tr_type)
     
-    def payoff_calculator():
-        x=spot*np.arange(100-spot_range,101+spot_range,0.01)/100
-        
-        y=[]
-        if str.lower(op_type)=='c':
-            for i in range(len(x)):
-                y.append(max((x[i]-strike-op_pr),-op_pr))
-        else:
-            for i in range(len(x)):
-                y.append(max(strike-x[i]-op_pr,-op_pr))
+    
+    x=spot*np.arange(100-spot_range,101+spot_range,0.01)/100
+    y = payoff_calculator(x, op_type, strike, op_pr, tr_type, contracts)
+
 
-        if str.lower(tr_type)=='s':
-            y=-np.array(y)
-        return x,y
-        
-    x,y=payoff_calculator()
     y0=np.zeros_like(x)
     
     def plotter(x,y):
diff --git a/opstrat/helpers.py b/opstrat/helpers.py
index 2e3e305..1844684 100644
--- a/opstrat/helpers.py
+++ b/opstrat/helpers.py
@@ -1,9 +1,10 @@
-import yfinance as yf
 import numpy as np
+import yfinance as yf
+
 
 def check_optype(op_type):
-    if (op_type not in ['p','c']):
-        raise ValueError("Input 'p' for put and 'c' for call!")
+    if (op_type not in ['p','c', 'bc', 'bp']):
+        raise ValueError("Input 'p' for put and 'c' for call, or 'bc'/'bp' for binaries!")
 
 def check_trtype(tr_type):
     if (tr_type not in ['b','s']):
@@ -14,9 +15,17 @@ def payoff_calculator(x, op_type, strike, op_pr, tr_type, n):
     if op_type=='c':
         for i in range(len(x)):
             y.append(max((x[i]-strike-op_pr),-op_pr))
-    else:
+    elif op_type=='p':
         for i in range(len(x)):
             y.append(max(strike-x[i]-op_pr,-op_pr))
+    elif op_type=='bc':
+        for i in range(len(x)):
+            y.append(1-op_pr if x[i] > strike else 0-op_pr)
+    elif op_type=='bp':
+        for i in range(len(x)):
+            y.append(1-op_pr if strike > x[i] else 0-op_pr)
+
+
     y=np.array(y)
 
     if tr_type=='s':