diff --git a/marketsim/MM/simMM.py b/marketsim/MM/simMM.py
index 092c2364..c8470a28 100644
--- a/marketsim/MM/simMM.py
+++ b/marketsim/MM/simMM.py
@@ -36,8 +36,12 @@ def __init__(self,
                  p=2,
                  k_min=5,
                  k_max=20,
-                 max_position=100
+                 max_position=100, 
+                 random_seed: int = None
                  ):
+        
+        random.seed(random_seed)
+        torch.seed(random.randint(1, 4096))
 
         if shade is None:
             shade = [250, 500]
@@ -71,8 +75,8 @@ def __init__(self,
 
         self.markets = []
         for _ in range(num_assets):
-            fundamental = LazyGaussianMeanReverting(mean=mean, final_time=sim_time+1, r=r, shock_var=shock_var)
-            self.markets.append(Market(fundamental=fundamental, time_steps=sim_time))
+            fundamental = LazyGaussianMeanReverting(mean=mean, final_time=sim_time+1, r=r, shock_var=shock_var, random_seed=random.randint(1, 4096))
+            self.markets.append(Market(fundamental=fundamental, time_steps=sim_time, random_seed=random.randint(1, 4096)))
 
         self.agents = {}
         for agent_id in range(num_background_agents):
@@ -85,8 +89,10 @@ def __init__(self,
                     market=self.markets[0],
                     q_max=q_max,
                     shade=shade,
-                    pv_var=pv_var
-                ))
+                    pv_var=pv_var,
+                    random_seed=random.randint(1,4096)
+                )
+            )
 
         self.arrivals_MM[self.arrival_times_MM[self.arrival_index_MM].item()].append(self.num_background_agents)
         self.arrival_index_MM += 1
@@ -106,7 +112,8 @@ def __init__(self,
                 p=p,
                 k_min=k_min,
                 k_max=k_max,
-                max_position=max_position
+                max_position=max_position,
+                random_seed=random.randint(1,4096)
             )
         else: # ladder policy
             self.MM = MMAgent(
@@ -114,7 +121,8 @@ def __init__(self,
                 market=self.markets[0],
                 K=K,
                 xi=xi,
-                omega=omega
+                omega=omega,
+                random_seed=random.randint(1,4096)
             )
         self.agents[self.num_background_agents] = self.MM
 
diff --git a/marketsim/agent/extented_zi_agent.py b/marketsim/agent/extented_zi_agent.py
index 1c7327f5..47ac3d05 100644
--- a/marketsim/agent/extented_zi_agent.py
+++ b/marketsim/agent/extented_zi_agent.py
@@ -8,10 +8,13 @@
 
 
 class ZIAgent(Agent):
-    def __init__(self, agent_id: int, market: Market, q_max: int, offset: float, eta: float, shade: List):
+    def __init__(self, agent_id: int, market: Market, q_max: int, pv_var: int, offset: float, eta: float, shade: List, random_seed: int = None):
+        
+        random.seed(random_seed)
+
         self.agent_id = agent_id
         self.market = market
-        self.pv = PrivateValues(q_max)
+        self.pv = PrivateValues(q_max, pv_var, random_seed=random.randint(1,4096))
         self.position = 0
         self.offset = offset
         self.eta = eta
diff --git a/marketsim/agent/hbl_agent.py b/marketsim/agent/hbl_agent.py
index 6e56efb3..b0663740 100644
--- a/marketsim/agent/hbl_agent.py
+++ b/marketsim/agent/hbl_agent.py
@@ -15,10 +15,14 @@
 
 class HBLAgent(Agent):
     def __init__(self, agent_id: int, market: Market, q_max: int, shade: List, L: int, pv_var: float,
-                 arrival_rate: float):
+                 arrival_rate: float, random_seed: int = None):
+        
+        random.seed(random_seed)
+        np.random.seed(random.randint(1,4096))
+
         self.agent_id = agent_id
         self.market = market
-        self.pv = PrivateValues(q_max, pv_var)
+        self.pv = PrivateValues(q_max, pv_var, random_seed=random.randint(1,4096))
         self.position = 0
         self.shade = shade
         self.cash = 0
diff --git a/marketsim/agent/market_maker.py b/marketsim/agent/market_maker.py
index 3bcef2e7..7b7ef784 100644
--- a/marketsim/agent/market_maker.py
+++ b/marketsim/agent/market_maker.py
@@ -8,7 +8,10 @@
 
 
 class MMAgent(Agent):
-    def __init__(self, agent_id: int, market: Market, xi: float, K: int, omega: float):
+    def __init__(self, agent_id: int, market: Market, xi: float, K: int, omega: float, random_seed: int = None):
+        
+        random.seed(random_seed)
+
         self.agent_id = agent_id
         self.market = market
 
diff --git a/marketsim/agent/market_maker_beta.py b/marketsim/agent/market_maker_beta.py
index 36dae715..6354bdce 100644
--- a/marketsim/agent/market_maker_beta.py
+++ b/marketsim/agent/market_maker_beta.py
@@ -65,7 +65,11 @@ def __init__(self,
                  p=2,
                  k_min=5,
                  k_max=20,
-                 max_position=15):
+                 max_position=15,
+                 random_seed: int = None):
+        
+        random.seed(random_seed)
+        np.random.seed(random.randint(1,4096))
 
         self.agent_id = agent_id
         self.market = market
diff --git a/marketsim/agent/spoofer.py b/marketsim/agent/spoofer.py
index 5cf030b2..0e566abb 100644
--- a/marketsim/agent/spoofer.py
+++ b/marketsim/agent/spoofer.py
@@ -7,10 +7,13 @@
 
 
 class SpoofingAgent(Agent):
-    def __init__(self, agent_id: int, market: Market, q_max: int, pv_var: float, order_size:int, spoofing_size: int, normalizers: dict):
+    def __init__(self, agent_id: int, market: Market, q_max: int, pv_var: float, order_size:int, spoofing_size: int, normalizers: dict, random_seed: int = None):
+                
+        random.seed(random_seed)
+
         self.agent_id = agent_id
         self.market = market
-        self.pv = PrivateValues(q_max, pv_var)
+        self.pv = PrivateValues(q_max, pv_var, random_seed=random.randint(1,4096))
         self.position = 0
         self.spoofing_size = spoofing_size
         self.order_size = order_size
@@ -75,7 +78,7 @@ def get_pos_value(self) -> float:
         return self.pv.value_at_position(self.position)
 
     def reset(self):
-        self.pv = PrivateValues(self.q_max, self.pv_var)
+        self.pv = PrivateValues(self.q_max, self.pv_var, random_seed=random.randint(1,4096))
         self.position = 0
         self.cash = 0
         self.last_value = 0
diff --git a/marketsim/agent/zero_intelligence_agent.py b/marketsim/agent/zero_intelligence_agent.py
index eaa987de..955a6171 100644
--- a/marketsim/agent/zero_intelligence_agent.py
+++ b/marketsim/agent/zero_intelligence_agent.py
@@ -9,12 +9,16 @@
 
 
 class ZIAgent(Agent):
-    def __init__(self, agent_id: int, market: Market, q_max: int, shade: List, pv_var: float, eta: float = 1.0):
+    def __init__(self, agent_id: int, market: Market, q_max: int, shade: List, pv_var: float, eta: float = 1.0, random_seed: int = None):
+        
+        random.seed(random_seed)
+        np.random.seed(random.randint(1, 4096))
+
         self.agent_id = agent_id
         self.market = market
         self.q_max = q_max
         self.pv_var = pv_var
-        self.pv = PrivateValues(q_max, pv_var)
+        self.pv = PrivateValues(q_max, pv_var, random_seed=random.randint(1, 4096))
         self.position = 0
         self.shade = shade
         self.cash = 0
@@ -86,5 +90,5 @@ def get_pos_value(self) -> float:
     def reset(self):
         self.position = 0
         self.cash = 0
-        self.pv = PrivateValues(self.q_max, self.pv_var)
+        self.pv = PrivateValues(self.q_max, self.pv_var, random_seed=random.randint(1, 4096))
 
diff --git a/marketsim/event/event_queue.py b/marketsim/event/event_queue.py
index 310d86ae..a38b0b36 100644
--- a/marketsim/event/event_queue.py
+++ b/marketsim/event/event_queue.py
@@ -6,8 +6,10 @@
 
 
 class EventQueue:
-    def __init__(self, rand_seed: int = None):
-        self.rand = random.Random(rand_seed)
+    def __init__(self, random_seed: int = None):
+        
+        random.seed(random_seed)
+        
         self.scheduled_activities = defaultdict(list)
         self.current_time = 0
 
@@ -27,3 +29,4 @@ def get_current_time(self):
 
     def set_time(self, t):
         self.current_time = t
+        
\ No newline at end of file
diff --git a/marketsim/fundamental/constant.py b/marketsim/fundamental/constant.py
index 24d1c569..18fca49d 100644
--- a/marketsim/fundamental/constant.py
+++ b/marketsim/fundamental/constant.py
@@ -1,10 +1,15 @@
+import random
 import torch
 
 from fundamental_abc import Fundamental
 
 
 class Constant(Fundamental):
-    def __init__(self, final_time: int, value: float):
+    def __init__(self, final_time: int, value: float, random_seed: int = None):
+               
+        random.seed(random_seed)
+        torch.manual_seed(random.randint(1, 4096))
+
         self.fundamental_values = torch.ones(final_time, dtype=torch.float32)*value
 
     def get_value_at(self, time: int) -> float:
diff --git a/marketsim/fundamental/lazy_mean_reverting.py b/marketsim/fundamental/lazy_mean_reverting.py
index 82ac32bb..58cec09c 100644
--- a/marketsim/fundamental/lazy_mean_reverting.py
+++ b/marketsim/fundamental/lazy_mean_reverting.py
@@ -1,3 +1,4 @@
+import random
 import torch
 from marketsim.fundamental.fundamental_abc import Fundamental
 
@@ -13,7 +14,11 @@ class LazyGaussianMeanReverting(Fundamental):
         shock_var (float): The variance of the Gaussian shocks.
         shock_mean (float, optional): The mean of the Gaussian shocks. Default is 0.
     """
-    def __init__(self, final_time: int, mean: float, r: float, shock_var: float, shock_mean: float = 0):
+    def __init__(self, final_time: int, mean: float, r: float, shock_var: float, shock_mean: float = 0, random_seed: int = None):
+        
+        random.seed(random_seed)
+        torch.manual_seed(random.randint(1, 4096))
+
         self.final_time = final_time
         self.mean = torch.tensor(mean, dtype=torch.float32)
         self.r = torch.tensor(r, dtype=torch.float32)
diff --git a/marketsim/fundamental/mean_reverting.py b/marketsim/fundamental/mean_reverting.py
index 37cfb6c5..43e947a7 100644
--- a/marketsim/fundamental/mean_reverting.py
+++ b/marketsim/fundamental/mean_reverting.py
@@ -1,9 +1,14 @@
+import random
 import torch
 from .fundamental_abc import Fundamental
 
 
 class GaussianMeanReverting(Fundamental):
-    def __init__(self, final_time: int, mean: float, r: float, shock_var: float, shock_mean: float = 0):
+    def __init__(self, final_time: int, mean: float, r: float, shock_var: float, shock_mean: float = 0, random_seed: int = None):
+        
+        random.seed(random_seed)
+        torch.manual_seed(random.randint(1, 4096))
+
         self.final_time = final_time
         self.mean = torch.tensor(mean, dtype=torch.float32)
         self.r = torch.tensor(r, dtype=torch.float32)
diff --git a/marketsim/intro_notebook.ipynb b/marketsim/intro_notebook.ipynb
index ba79e69c..b2d6b5cb 100644
--- a/marketsim/intro_notebook.ipynb
+++ b/marketsim/intro_notebook.ipynb
@@ -5,11 +5,11 @@
    "execution_count": 1,
    "id": "initial_id",
    "metadata": {
-    "collapsed": true,
     "ExecuteTime": {
      "end_time": "2024-02-15T04:09:48.703057400Z",
      "start_time": "2024-02-15T04:09:48.664059300Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -20,6 +20,15 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
+   "id": "94fd9db23a88d5c5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:09:49.138070Z",
+     "start_time": "2024-02-15T04:09:49.136069300Z"
+    },
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "# Let's start with order\n",
@@ -28,41 +37,43 @@
     "order2 = Order(price=32, order_type=SELL, quantity=22, time=1, agent_id=1, order_id=2)\n",
     "order3 = Order(price=7, order_type=SELL, quantity=7, time=1, agent_id=1, order_id=3)\n",
     "order4 = Order(price=9, order_type=SELL, quantity=8, time=1, agent_id=1, order_id=4)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:09:49.138070Z",
-     "start_time": "2024-02-15T04:09:49.136069300Z"
-    }
-   },
-   "id": "94fd9db23a88d5c5",
-   "execution_count": 2
+   ]
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "# Now let's see initialize the fourheap\n",
-    "\n",
-    "fh = FourHeap() "
-   ],
+   "execution_count": 3,
+   "id": "cc7b3ae1ebf76ee1",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-02-15T04:09:49.335085500Z",
      "start_time": "2024-02-15T04:09:49.333084400Z"
-    }
+    },
+    "collapsed": false
    },
-   "id": "cc7b3ae1ebf76ee1",
-   "execution_count": 3
+   "outputs": [],
+   "source": [
+    "# Now let's see initialize the fourheap\n",
+    "\n",
+    "fh = FourHeap() "
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
+   "id": "361113b24009b04e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:09:49.582092300Z",
+     "start_time": "2024-02-15T04:09:49.577092900Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "{1: Order(price=12, order_type=1, quantity=12, agent_id=1, time=1, order_id=1, asset_id=1)}"
+      "text/plain": [
+       "{1: Order(price=12, order_type=1, quantity=12, agent_id=1, time=1, order_id=1, asset_id=1)}"
+      ]
      },
      "execution_count": 4,
      "metadata": {},
@@ -74,23 +85,25 @@
     "\n",
     "fh.insert(order1)\n",
     "fh.buy_unmatched.order_dict"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:09:49.582092300Z",
-     "start_time": "2024-02-15T04:09:49.577092900Z"
-    }
-   },
-   "id": "361113b24009b04e",
-   "execution_count": 4
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
+   "id": "ebe4a5b482eb8d70",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:09:49.844940900Z",
+     "start_time": "2024-02-15T04:09:49.791936400Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "{2: Order(price=32, order_type=-1, quantity=22, agent_id=1, time=1, order_id=2, asset_id=1)}"
+      "text/plain": [
+       "{2: Order(price=32, order_type=-1, quantity=22, agent_id=1, time=1, order_id=2, asset_id=1)}"
+      ]
      },
      "execution_count": 5,
      "metadata": {},
@@ -105,19 +118,19 @@
     "# Because it's price is higher than the buy price this won't cause a match\n",
     "\n",
     "fh.sell_unmatched.order_dict"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:09:49.844940900Z",
-     "start_time": "2024-02-15T04:09:49.791936400Z"
-    }
-   },
-   "id": "ebe4a5b482eb8d70",
-   "execution_count": 5
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
+   "id": "5b93a62e48ad867e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:09:50.031942Z",
+     "start_time": "2024-02-15T04:09:49.985942100Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -141,19 +154,19 @@
     "# Since order 1 is larger some will be matched and some will be unmatched\n",
     "print(fh.buy_unmatched.order_dict)\n",
     "print(fh.buy_matched.order_dict)\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:09:50.031942Z",
-     "start_time": "2024-02-15T04:09:49.985942100Z"
-    }
-   },
-   "id": "5b93a62e48ad867e",
-   "execution_count": 6
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
+   "id": "ae2bfd9555f5e434",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:09:50.303405Z",
+     "start_time": "2024-02-15T04:09:50.253396300Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -175,19 +188,19 @@
     "print(fh.sell_matched.order_dict)\n",
     "print(fh.buy_unmatched.order_dict)\n",
     "print(fh.buy_matched.order_dict)\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:09:50.303405Z",
-     "start_time": "2024-02-15T04:09:50.253396300Z"
-    }
-   },
-   "id": "ae2bfd9555f5e434",
-   "execution_count": 7
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
+   "id": "3ea852481fc4c008",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:09:50.524404700Z",
+     "start_time": "2024-02-15T04:09:50.482405500Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -208,19 +221,19 @@
     "print(fh.sell_matched.order_dict)\n",
     "print(fh.sell_unmatched.order_dict)\n",
     "print(fh.buy_matched.order_dict)\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:09:50.524404700Z",
-     "start_time": "2024-02-15T04:09:50.482405500Z"
-    }
-   },
-   "id": "3ea852481fc4c008",
-   "execution_count": 8
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
+   "id": "a13c3435bf208848",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:09:50.906926200Z",
+     "start_time": "2024-02-15T04:09:50.864927100Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -242,19 +255,19 @@
     "print(fh.sell_unmatched.order_dict)\n",
     "print(fh.buy_matched.order_dict)\n",
     "print(fh.buy_unmatched.order_dict)\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:09:50.906926200Z",
-     "start_time": "2024-02-15T04:09:50.864927100Z"
-    }
-   },
-   "id": "a13c3435bf208848",
-   "execution_count": 9
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
+   "id": "bcd1634ff00877e8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:11:22.295305500Z",
+     "start_time": "2024-02-15T04:11:22.248306800Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -285,19 +298,19 @@
     "print(fh.sell_unmatched.order_dict)\n",
     "print(fh.buy_matched.order_dict)\n",
     "print(fh.buy_unmatched.order_dict)\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:11:22.295305500Z",
-     "start_time": "2024-02-15T04:11:22.248306800Z"
-    }
-   },
-   "id": "bcd1634ff00877e8",
-   "execution_count": 11
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
+   "id": "fe3c9bd1cdefe8d8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:11:56.266000300Z",
+     "start_time": "2024-02-15T04:11:56.220995Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -317,19 +330,19 @@
     "print(fh.sell_unmatched.order_dict)\n",
     "print(fh.buy_matched.order_dict)\n",
     "print(fh.buy_unmatched.order_dict)\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:11:56.266000300Z",
-     "start_time": "2024-02-15T04:11:56.220995Z"
-    }
-   },
-   "id": "fe3c9bd1cdefe8d8",
-   "execution_count": 12
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
+   "id": "d49d7335bdfc4f4d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:35:44.877998600Z",
+     "start_time": "2024-02-15T04:35:44.870998500Z"
+    },
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "from fundamental.lazy_mean_reverting import LazyGaussianMeanReverting\n",
@@ -337,23 +350,25 @@
     "# Let's look at the fundamental next\n",
     "\n",
     "f = LazyGaussianMeanReverting(final_time=100, mean=12, r=.2, shock_var=.01)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:35:44.877998600Z",
-     "start_time": "2024-02-15T04:35:44.870998500Z"
-    }
-   },
-   "id": "d49d7335bdfc4f4d",
-   "execution_count": 7
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
+   "id": "cc2bd7a5d3a7ad3f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:35:46.433997800Z",
+     "start_time": "2024-02-15T04:35:46.430998500Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "{0: 12}"
+      "text/plain": [
+       "{0: 12}"
+      ]
      },
      "execution_count": 8,
      "metadata": {},
@@ -364,19 +379,19 @@
     "# The fundamental starts at the mean\n",
     "\n",
     "f.fundamental_values"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:35:46.433997800Z",
-     "start_time": "2024-02-15T04:35:46.430998500Z"
-    }
-   },
-   "id": "cc2bd7a5d3a7ad3f",
-   "execution_count": 8
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
+   "id": "b902e6769c663d52",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:35:46.900183400Z",
+     "start_time": "2024-02-15T04:35:46.897184600Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -392,19 +407,19 @@
     "f.get_value_at(12)\n",
     "\n",
     "print(f.fundamental_values)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:35:46.900183400Z",
-     "start_time": "2024-02-15T04:35:46.897184600Z"
-    }
-   },
-   "id": "b902e6769c663d52",
-   "execution_count": 9
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
+   "id": "6bdef9c2a15fd033",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T04:35:47.745318600Z",
+     "start_time": "2024-02-15T04:35:47.742319600Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -420,25 +435,17 @@
     "f.get_final_fundamental()\n",
     "\n",
     "print(f.fundamental_values)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-15T04:35:47.745318600Z",
-     "start_time": "2024-02-15T04:35:47.742319600Z"
-    }
-   },
-   "id": "6bdef9c2a15fd033",
-   "execution_count": 10
+   ]
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [],
+   "execution_count": null,
+   "id": "ba5a64455c821680",
    "metadata": {
     "collapsed": false
    },
-   "id": "ba5a64455c821680"
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -457,7 +464,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/marketsim/market/market.py b/marketsim/market/market.py
index ae7afe77..75e119ad 100644
--- a/marketsim/market/market.py
+++ b/marketsim/market/market.py
@@ -1,3 +1,4 @@
+import random
 from marketsim.event.event_queue import EventQueue
 from marketsim.fourheap.fourheap import FourHeap
 from marketsim.fundamental.fundamental_abc import Fundamental
@@ -5,11 +6,14 @@
 
 
 class Market:
-    def __init__(self, fundamental: Fundamental, time_steps):
+    def __init__(self, fundamental: Fundamental, time_steps,  random_seed: int = None):
+   
+        random.seed(random_seed)
+
         self.order_book = FourHeap()
         self.matched_orders = []
         self.fundamental = fundamental
-        self.event_queue = EventQueue()
+        self.event_queue = EventQueue(random_seed=random.randint(1,4096))
         self.end_time = time_steps
 
 
@@ -58,5 +62,5 @@ def get_midprices(self):
     def reset(self, fundamental):
         self.order_book = FourHeap()
         self.matched_orders = []
-        self.event_queue = EventQueue()
+        self.event_queue = EventQueue(random_seed=random.randint(1,4096))
         self.fundamental = fundamental
diff --git a/marketsim/private_values/private_values.py b/marketsim/private_values/private_values.py
index c8179bae..2894f35a 100644
--- a/marketsim/private_values/private_values.py
+++ b/marketsim/private_values/private_values.py
@@ -1,3 +1,4 @@
+import random
 import torch
 from marketsim.fourheap.constants import BUY, SELL
 
@@ -12,13 +13,17 @@ class PrivateValues:
     as well as calculate the cumulative value up to a given position.
     """
 
-    def __init__(self, q_max: int, val_var=5e6):
+    def __init__(self, q_max: int, val_var=5e6, random_seed: int = None):
         """
         Initialize the PrivateValues object.
 
         :param q_max: The maximum quantity.
         :param val_var: The variance of the values (default: 1).
         """
+        
+        random.seed(random_seed)
+        torch.manual_seed(random.randint(1, 4096))
+
         self.values = torch.randn(2 * q_max) * torch.sqrt(torch.tensor(val_var))
         self.values, _ = self.values.sort(descending=True)
 
@@ -27,6 +32,7 @@ def __init__(self, q_max: int, val_var=5e6):
         self.extra_buy = min(self.values[-1].item(), 0)
         self.extra_sell = max(self.values[0].item(), 0)
 
+
     def value_for_exchange(self, position: int, order_type: int) -> float:
         """
         Calculates the value associated with a given trade and order type.
diff --git a/marketsim/simulator/sampled_arrival_simulator.py b/marketsim/simulator/sampled_arrival_simulator.py
index 77ba51b4..6eadb72e 100644
--- a/marketsim/simulator/sampled_arrival_simulator.py
+++ b/marketsim/simulator/sampled_arrival_simulator.py
@@ -1,11 +1,12 @@
 import random
-from fourheap.constants import BUY, SELL
-from market.market import Market
-from fundamental.lazy_mean_reverting import LazyGaussianMeanReverting
-from agent.zero_intelligence_agent import ZIAgent
-from agent.hbl_agent import HBLAgent
+from marketsim.fourheap.constants import BUY, SELL
+from marketsim.market.market import Market
+from marketsim.fundamental.lazy_mean_reverting import LazyGaussianMeanReverting
+from marketsim.agent.zero_intelligence_agent import ZIAgent
+from marketsim.agent.hbl_agent import HBLAgent
 import torch.distributions as dist
 import torch
+import numpy as np
 from collections import defaultdict
 
 
@@ -23,9 +24,16 @@ def __init__(self,
                  shade=None,
                  eta: float = 0.2,
                  hbl_agent: bool = False,
-                 lam_r: float = None
+                 lam_r: float = None,
+                 random_seed: int = None
                  ):
+        
 
+        random.seed(random_seed)
+        torch.manual_seed(random.randint(1, 4096))
+        np.random.seed(random.randint(1, 4096))
+
+        
         if shade is None:
             shade = [10, 30]
         if lam_r is None:
@@ -47,8 +55,8 @@ def __init__(self,
 
         self.markets = []
         for _ in range(num_assets):
-            fundamental = LazyGaussianMeanReverting(mean=mean, final_time=sim_time, r=r, shock_var=shock_var)
-            self.markets.append(Market(fundamental=fundamental, time_steps=sim_time))
+            fundamental = LazyGaussianMeanReverting(mean=mean, final_time=sim_time, r=r, shock_var=shock_var, random_seed=random.randint(1,4096))
+            self.markets.append(Market(fundamental=fundamental, time_steps=sim_time, random_seed=random.randint(1,4096)))
 
         self.agents = {}
         # TEMP FOR HBL TESTING
@@ -63,9 +71,11 @@ def __init__(self,
                         q_max=q_max,
                         shade=shade,
                         pv_var=pv_var,
-                        eta=eta
+                        eta=eta,
+                        random_seed=random.randint(1,4096)
                     ))
-        #  expanded_zi
+
+        # expanded_zi
         # else:
         #     for agent_id in range(24):
         #         self.arrivals[self.arrival_times[self.arrival_index].item()].append(agent_id)
diff --git a/marketsim/simulator/simulator.py b/marketsim/simulator/simulator.py
index 70348329..1fead966 100644
--- a/marketsim/simulator/simulator.py
+++ b/marketsim/simulator/simulator.py
@@ -6,7 +6,6 @@
 from marketsim.agent.zero_intelligence_agent import ZIAgent
 
 
-
 class Simulator:
     def __init__(self,
                  num_background_agents: int,
@@ -17,7 +16,13 @@ def __init__(self,
                  r: float = .6,
                  shock_var=10,
                  q_max: int = 10,
-                 zi_shade: List = [10, 30]):
+                 zi_shade: List = [10, 30],
+                 random_seed: int = None
+                 ):
+        
+       
+        random.seed(random_seed)
+        
         self.num_agents = num_background_agents
         self.num_assets = num_assets
         self.sim_time = sim_time
@@ -26,9 +31,9 @@ def __init__(self,
 
         self.markets = []
         for _ in range(num_assets):
-            fundamental = GaussianMeanReverting(mean=mean, final_time=sim_time, r=r, shock_var=shock_var)
+            fundamental = GaussianMeanReverting(mean=mean, final_time=sim_time, r=r, shock_var=shock_var, random_seed=random.randint(1,4096))
             # fundamental = LazyGaussianMeanReverting(mean=mean, final_time=sim_time, r=r, shock_var=shock_var)
-            self.markets.append(Market(fundamental=fundamental, time_steps=sim_time))
+            self.markets.append(Market(fundamental=fundamental, time_steps=sim_time, random_seed=random.randint(1,4096)))
 
         self.agents = {}
         for agent_id in range(num_background_agents):
@@ -37,7 +42,8 @@ def __init__(self,
                     agent_id=agent_id,
                     market=self.markets[0],
                     q_max=q_max,
-                    shade=[10, 30]
+                    shade=[10, 30],
+                    random_seed=random.randint(1,4096)
                 ))
 
     def step(self):
diff --git a/marketsim/test_event_queue.ipynb b/marketsim/test_event_queue.ipynb
index 4e514273..e18e461d 100644
--- a/marketsim/test_event_queue.ipynb
+++ b/marketsim/test_event_queue.ipynb
@@ -5,11 +5,11 @@
    "execution_count": 1,
    "id": "initial_id",
    "metadata": {
-    "collapsed": true,
     "ExecuteTime": {
      "end_time": "2023-11-02T02:00:54.071626Z",
      "start_time": "2023-11-02T02:00:52.890317Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -23,62 +23,72 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "outputs": [],
-   "source": [
-    "e = EventQueue()"
-   ],
+   "id": "adf6ffdda3d8a689",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2023-11-02T02:00:55.364165Z",
      "start_time": "2023-11-02T02:00:55.357739Z"
-    }
+    },
+    "collapsed": false
    },
-   "id": "adf6ffdda3d8a689"
+   "outputs": [],
+   "source": [
+    "e = EventQueue()"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "outputs": [],
-   "source": [
-    "order1 = Order(price=1, quantity=1, time=1, agent_id=1, order_id=1, order_type=BUY)\n",
-    "order2 = Order(price=1, quantity=1, time=1, agent_id=1, order_id=2, order_type=BUY)\n",
-    "order3 = Order(price=1, quantity=1, time=3, agent_id=1, order_id=3, order_type=SELL)\n"
-   ],
+   "id": "acd56cd67811e74d",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2023-11-02T02:00:55.631574Z",
      "start_time": "2023-11-02T02:00:55.627375Z"
-    }
+    },
+    "collapsed": false
    },
-   "id": "acd56cd67811e74d"
+   "outputs": [],
+   "source": [
+    "order1 = Order(price=1, quantity=1, time=1, agent_id=1, order_id=1, order_type=BUY)\n",
+    "order2 = Order(price=1, quantity=1, time=1, agent_id=1, order_id=2, order_type=BUY)\n",
+    "order3 = Order(price=1, quantity=1, time=3, agent_id=1, order_id=3, order_type=SELL)\n"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "outputs": [],
-   "source": [
-    "e.schedule_activity(order1)\n",
-    "e.schedule_activity(order2)\n",
-    "e.schedule_activity(order3)\n"
-   ],
+   "id": "e81b92116b7c7b9d",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2023-11-02T02:00:55.846956Z",
      "start_time": "2023-11-02T02:00:55.845125Z"
-    }
+    },
+    "collapsed": false
    },
-   "id": "e81b92116b7c7b9d"
+   "outputs": [],
+   "source": [
+    "e.schedule_activity(order1)\n",
+    "e.schedule_activity(order2)\n",
+    "e.schedule_activity(order3)\n"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "2b15b5866d781227",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-02T02:00:56.086065Z",
+     "start_time": "2023-11-02T02:00:56.051275Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "[]"
+      "text/plain": [
+       "[]"
+      ]
      },
      "execution_count": 5,
      "metadata": {},
@@ -87,23 +97,26 @@
    ],
    "source": [
     "e.step()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-02T02:00:56.086065Z",
-     "start_time": "2023-11-02T02:00:56.051275Z"
-    }
-   },
-   "id": "2b15b5866d781227"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "eba7945d5a7f5263",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-02T02:00:56.227455Z",
+     "start_time": "2023-11-02T02:00:56.224831Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "[Order(price=1, order_type=1, quantity=1, agent_id=1, time=1, order_id=1, asset_id=1),\n Order(price=1, order_type=1, quantity=1, agent_id=1, time=1, order_id=2, asset_id=1)]"
+      "text/plain": [
+       "[Order(price=1, order_type=1, quantity=1, agent_id=1, time=1, order_id=1, asset_id=1),\n",
+       " Order(price=1, order_type=1, quantity=1, agent_id=1, time=1, order_id=2, asset_id=1)]"
+      ]
      },
      "execution_count": 6,
      "metadata": {},
@@ -112,23 +125,25 @@
    ],
    "source": [
     "e.step()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-02T02:00:56.227455Z",
-     "start_time": "2023-11-02T02:00:56.224831Z"
-    }
-   },
-   "id": "eba7945d5a7f5263"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "8ac0f93a500647fb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-02T02:00:56.439237Z",
+     "start_time": "2023-11-02T02:00:56.434962Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "[]"
+      "text/plain": [
+       "[]"
+      ]
      },
      "execution_count": 7,
      "metadata": {},
@@ -137,55 +152,57 @@
    ],
    "source": [
     "e.step()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-02T02:00:56.439237Z",
-     "start_time": "2023-11-02T02:00:56.434962Z"
-    }
-   },
-   "id": "8ac0f93a500647fb"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "outputs": [],
-   "source": [
-    "f = GaussianMeanReverting(mean=100, r=0.2, final_time=100, shock_var=10)"
-   ],
+   "id": "88cbe58aba097195",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2023-11-02T02:00:56.645679Z",
      "start_time": "2023-11-02T02:00:56.640489Z"
-    }
+    },
+    "collapsed": false
    },
-   "id": "88cbe58aba097195"
+   "outputs": [],
+   "source": [
+    "f = GaussianMeanReverting(mean=100, r=0.2, final_time=100, shock_var=10)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "outputs": [],
-   "source": [
-    "m = Market(f)"
-   ],
+   "id": "b19ca32418bbd288",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2023-11-02T02:00:57.178505Z",
      "start_time": "2023-11-02T02:00:57.176993Z"
-    }
+    },
+    "collapsed": false
    },
-   "id": "b19ca32418bbd288"
+   "outputs": [],
+   "source": [
+    "m = Market(f)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "910dcd209c37965",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-02T02:00:57.896940Z",
+     "start_time": "2023-11-02T02:00:57.883064Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "100.0"
+      "text/plain": [
+       "100.0"
+      ]
      },
      "execution_count": 10,
      "metadata": {},
@@ -194,57 +211,64 @@
    ],
    "source": [
     "m.get_fundamental_value()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-02T02:00:57.896940Z",
-     "start_time": "2023-11-02T02:00:57.883064Z"
-    }
-   },
-   "id": "910dcd209c37965"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "outputs": [],
-   "source": [
-    "m.add_order(order1)\n",
-    "m.add_order(order2)\n",
-    "m.add_order(order3)\n"
-   ],
+   "id": "fecf8d0b5d2b0d76",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2023-11-02T02:01:34.513870Z",
      "start_time": "2023-11-02T02:01:34.507170Z"
-    }
+    },
+    "collapsed": false
    },
-   "id": "fecf8d0b5d2b0d76"
+   "outputs": [],
+   "source": [
+    "m.add_order(order1)\n",
+    "m.add_order(order2)\n",
+    "m.add_order(order3)\n"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
-   "outputs": [],
-   "source": [
-    "m.step()"
-   ],
+   "id": "5592a8bef91a39dd",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2023-11-02T02:02:15.127481Z",
      "start_time": "2023-11-02T02:02:15.119847Z"
-    }
+    },
+    "collapsed": false
    },
-   "id": "5592a8bef91a39dd"
+   "outputs": [],
+   "source": [
+    "m.step()"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
+   "id": "3ec35fc46b4dfdaf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-02T02:02:15.439666Z",
+     "start_time": "2023-11-02T02:02:15.435653Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "defaultdict(list,\n            {1: [Order(price=1, order_type=1, quantity=1, agent_id=1, time=1, order_id=1, asset_id=1),\n              Order(price=1, order_type=1, quantity=1, agent_id=1, time=1, order_id=2, asset_id=1)],\n             3: [Order(price=1, order_type=-1, quantity=1, agent_id=1, time=3, order_id=3, asset_id=1)],\n             0: [],\n             2: []})"
+      "text/plain": [
+       "defaultdict(list,\n",
+       "            {1: [Order(price=1, order_type=1, quantity=1, agent_id=1, time=1, order_id=1, asset_id=1),\n",
+       "              Order(price=1, order_type=1, quantity=1, agent_id=1, time=1, order_id=2, asset_id=1)],\n",
+       "             3: [Order(price=1, order_type=-1, quantity=1, agent_id=1, time=3, order_id=3, asset_id=1)],\n",
+       "             0: [],\n",
+       "             2: []})"
+      ]
      },
      "execution_count": 25,
      "metadata": {},
@@ -253,23 +277,25 @@
    ],
    "source": [
     "m.event_queue.scheduled_activities"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-02T02:02:15.439666Z",
-     "start_time": "2023-11-02T02:02:15.435653Z"
-    }
-   },
-   "id": "3ec35fc46b4dfdaf"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
+   "id": "f226ab58891b1b9f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-02T02:02:16.395887Z",
+     "start_time": "2023-11-02T02:02:16.353163Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "4"
+      "text/plain": [
+       "4"
+      ]
      },
      "execution_count": 26,
      "metadata": {},
@@ -278,25 +304,17 @@
    ],
    "source": [
     "m.get_time()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-02T02:02:16.395887Z",
-     "start_time": "2023-11-02T02:02:16.353163Z"
-    }
-   },
-   "id": "f226ab58891b1b9f"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [],
+   "id": "2747f8bda9e21ed1",
    "metadata": {
     "collapsed": false
    },
-   "id": "2747f8bda9e21ed1"
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -315,7 +333,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/test_sim.ipynb b/test_sim.ipynb
index 7aa739d6..2c073b3c 100644
--- a/test_sim.ipynb
+++ b/test_sim.ipynb
@@ -2,16 +2,29 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "initial_id",
    "metadata": {
-    "collapsed": true,
     "ExecuteTime": {
      "end_time": "2024-03-27T23:39:39.806325Z",
      "start_time": "2024-03-27T23:39:37.947319Z"
-    }
+    },
+    "collapsed": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'fourheap'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmarketsim\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulator\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msampled_arrival_simulator\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SimulatorSampledArrival\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnotebook\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n",
+      "File \u001b[0;32m~/Code/SURE-SRG/market-sim-py/marketsim/simulator/sampled_arrival_simulator.py:2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrandom\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfourheap\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstants\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BUY, SELL\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmarket\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmarket\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Market\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfundamental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlazy_mean_reverting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LazyGaussianMeanReverting\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fourheap'"
+     ]
+    }
+   ],
    "source": [
     "from marketsim.simulator.sampled_arrival_simulator import SimulatorSampledArrival\n",
     "from tqdm.notebook import tqdm"
@@ -20,34 +33,44 @@
   {
    "cell_type": "code",
    "execution_count": 3,
+   "id": "72ae6676eeab193e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-25T08:07:38.106565700Z",
+     "start_time": "2024-03-25T08:07:38.103557300Z"
+    },
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "# %%timeit\n",
     "# \n",
     "# sim = Simulator(num_agents=66, sim_time=60000, lam=1e-4, mean=1e7, r=.05, shock_var=1e6)\n",
     "# sim.run()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-03-25T08:07:38.106565700Z",
-     "start_time": "2024-03-25T08:07:38.103557300Z"
-    }
-   },
-   "id": "72ae6676eeab193e"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
+   "id": "a68010fcb5fc866b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-25T08:38:17.892762800Z",
+     "start_time": "2024-03-25T08:35:52.049473800Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "  0%|          | 0/10000 [00:00<?, ?it/s]",
       "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae8a45d9090a4cb396885cc8201214fd",
        "version_major": 2,
-       "version_minor": 0,
-       "model_id": "ae8a45d9090a4cb396885cc8201214fd"
-      }
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -86,27 +109,30 @@
     "        values.append(value)\n",
     "    surpluses.append(sum(values)/len(values))\n",
     "print(sum(surpluses)/len(surpluses)*25)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-03-25T08:38:17.892762800Z",
-     "start_time": "2024-03-25T08:35:52.049473800Z"
-    }
-   },
-   "id": "a68010fcb5fc866b"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 29,
+   "id": "2090899632c545a9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-25T08:47:01.079261600Z",
+     "start_time": "2024-03-25T08:42:17.296566Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "  0%|          | 0/10000 [00:00<?, ?it/s]",
       "application/vnd.jupyter.widget-view+json": {
+       "model_id": "952411a1e21d47e695dd266c35da1b37",
        "version_major": 2,
-       "version_minor": 0,
-       "model_id": "952411a1e21d47e695dd266c35da1b37"
-      }
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -145,28 +171,30 @@
     "        values.append(value)\n",
     "    surpluses.append(sum(values)/len(values))\n",
     "print(sum(surpluses)/len(surpluses)*25)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-03-25T08:47:01.079261600Z",
-     "start_time": "2024-03-25T08:42:17.296566Z"
-    }
-   },
-   "id": "2090899632c545a9",
-   "execution_count": 29
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 28,
+   "id": "5de51e0d65a858cd",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-25T08:41:32.234485300Z",
+     "start_time": "2024-03-25T08:40:26.996067200Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "  0%|          | 0/10000 [00:00<?, ?it/s]",
       "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c1f9f0dfc834b4a8fbc93b00c8b7711",
        "version_major": 2,
-       "version_minor": 0,
-       "model_id": "5c1f9f0dfc834b4a8fbc93b00c8b7711"
-      }
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -205,19 +233,19 @@
     "        values.append(value)\n",
     "    surpluses.append(sum(values)/len(values))\n",
     "print(sum(surpluses)/len(surpluses)*25)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-03-25T08:41:32.234485300Z",
-     "start_time": "2024-03-25T08:40:26.996067200Z"
-    }
-   },
-   "id": "5de51e0d65a858cd",
-   "execution_count": 28
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 32,
+   "id": "da028cd7fb618978",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-26T20:44:46.314860800Z",
+     "start_time": "2024-03-26T20:44:46.196664700Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -228,8 +256,10 @@
     },
     {
      "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -275,19 +305,19 @@
     "plt.ylabel('Value')\n",
     "plt.title(f'Agent Position vs Value at fundamental {fundamental_val}')\n",
     "plt.show()\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-03-26T20:44:46.314860800Z",
-     "start_time": "2024-03-26T20:44:46.196664700Z"
-    }
-   },
-   "id": "da028cd7fb618978",
-   "execution_count": 32
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 38,
+   "id": "44a74cbe8acf673",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-26T20:44:58.709570600Z",
+     "start_time": "2024-03-26T20:44:58.596987100Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -298,8 +328,10 @@
     },
     {
      "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -345,19 +377,19 @@
     "plt.ylabel('Value')\n",
     "plt.title(f'Agent Position vs Value at fundamental {fundamental_val}')\n",
     "plt.show()\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-03-26T20:44:58.709570600Z",
-     "start_time": "2024-03-26T20:44:58.596987100Z"
-    }
-   },
-   "id": "44a74cbe8acf673",
-   "execution_count": 38
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
+   "id": "31f002d1c196705a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-27T23:40:00.547410Z",
+     "start_time": "2024-03-27T23:40:00.470896Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -368,8 +400,10 @@
     },
     {
      "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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Ljh07JrUMGJvuWDNmzNDb/uabbwJAqb/S1Wo1unbtKt2vW7cumjZtij///LPC4/Tq1Qt16tTRa7K/ffs2YmNjMXz4cGmbi4sLbty4UWYXzOOMHj0a+fn52LZtm7RN12L0cDP2w3893759G7m5uejatateN+TTiI2NRU5ODl5++WXpM/j333/D2toaHTp0wKFDhyp8vK2tLaysSr6miouLcevWLTg5OaFp06ZGy/FpDB8+HLVq1ZLu6z4Pus+A7vMdFhYGlUolxfXu3RtqtbrU81Xm/XjhhRf0Wi90M1MHDx4MZ2fnUtt1OSUmJuLSpUsYOXIkbt26Jb0nd+/exQsvvID4+HhotVq9Y02aNEnvfteuXXHr1i1oNJoKXp3S51RUVIRbt26hcePGcHFxeaL38LXXXsP/+3//D+PHj0dycjKSkpIwduxYpKenAwDu378PAHjxxRfh4+ODmTNnYtu2bbh27Rq+//57/Pvf/0aNGjWkuLJs2rQJNWvWxLBhwx6bz5QpU5CcnIwvvvgCNWrUqPT5lOfWrVuYN28e5s6dK3X9G8oYOdna2uK5557DkCFD8N1332HDhg1o164dRo8ejaNHj5b7uK1bt6KwsLDM7rI33ngDy5cvx8iRIzF48GAsW7YM69atw6VLl7By5conytOYWBBZgOLiYmzevBk9e/ZESkoKLl++jMuXL6NDhw7IzMzEgQMHpNhr167B09NTavbVady4sd79y5cvQwgh/Wd6+BYVFQUAyMrK0ntM/fr19e7rvuhv3779VOfXv39/xMbGYv/+/Th27Bj+/vtvfPLJJ7CyspKKraZNm5Z6XPPmzaUvagBYvHgxkpKS4O3tjfbt22P+/PmPLT4q49q1a7Cysir1Wnp4eMDFxaVUYfjo6wWUvGaPe71q1KiBwYMHY+fOnVKz/rZt21BUVKRXEM2ePRtOTk5o3749mjRpgoiIiFJjPMrTp08fuLq66nWbfffdd2jVqpXeeIPdu3ejY8eOsLOzg6urq9TNmJuba9BxHufSpUsASorARz+HP//8c6nP4KO0Wi0+/fRTNGnSBLa2tqhTpw7q1q2LM2fOGC3Hp/G4/zO6z0yTJk1KPbasz3xl3o9Hj60ruLy9vcvcrstJ956EhYWVek/+85//oKCgoNTxnua74f79+5g3bx68vb313sOcnJwneg8nTZqEd955B5s2bUKLFi3g7++PK1euSN04Tk5OAAA7Ozvs2bMHtWvXxuDBg9GgQQOMHTsW8+bNg6urqxT3qDt37mDnzp0IDg7WG6dUliVLlmDNmjV477338OKLL1b6XCoyZ84cuLq6YurUqZV6nLFymjJlCmJiYrB582aMGDECo0aNwv79++Hp6Yk33nij3Mdt3LgRrq6u6NOnj0HHGTlyJDw8PCpcKsRUjFfO0hM7ePAg0tPTsXnzZmzevLnU/o0bNyIoKKhSz6n7C2/mzJkIDg4uM+bRH35ra+sy48RTDnyrV68eAgMDn+o5gJJxDF27dsX27dvx888/Y8mSJVi0aBG2bdtm8H8+Qxi6WOPTvF4jRozAl19+iZ9++gkDBgzA999/j2bNmqFVq1ZSTPPmzXHx4kXs3r0be/fuxX//+1+sXLkS8+bN0xu/URbdX7dr1qxBZmYmUlNTcenSJSxevFiK+eWXX/DSSy+hW7duWLlyJTw9PVGzZk2sXbv2sQO3y3uNHl33RPc5XL9+PTw8PErFP+6v1w8//BBz587FK6+8gvfeew+urq6wsrLCtGnTSrViGINCoSjz/StrPRfAuP9nKvt+lHfsx+Wke92WLFmC1q1blxn7aLHwNOc5depUrF27FtOmTUNAQABUKhUUCgVGjBjxxO/hBx98gJkzZ+LcuXNQqVTw9/eXxoU999xzUlyLFi2QlJSE5ORk3L59WxqAPn36dGlg/aN27NiBe/fulTsgWCc6OhqzZ8/GpEmTMGfOnCc6j/JcunQJX331FZYtW6bXIp6fn4+ioiJcvXoVSqVSr1XdmDkVFhbi66+/xltvvSW10AIl3yt9+vTBF198gcLCQtjY2Og9LjU1Fb/88gteffVV1KxZ0+DjeXt7Izs7+4nzNRYWRBZg48aNcHNzw4oVK0rt27ZtG7Zv347Vq1fD3t4ePj4+OHToEO7du6fXSnT58mW9xzVs2BBAyQfYGMWIjrFXdtYthnfx4sVS+y5cuIA6derA0dFR2ubp6YnXXnsNr732GrKystCmTRt88MEH5RZElcnXx8cHWq0Wly5dQvPmzaXtmZmZyMnJMerCfd26dYOnpye2bNmCLl264ODBg2UO7HV0dMTw4cMxfPhwFBYWYtCgQfjggw8QGRn52MXMRo0ahdWrV2PLli1ISUmBQqHAyy+/LO3/73//Czs7O+zbt09vJsnatWsfm7+uhSAnJwcuLi7S9kdb0Ro1agQAcHNze6LP4datW9GzZ098/fXXettzcnL0Bosa63NZq1atMlsdn7TbWPeZ0bXKPOzRz/zTvB+VoXtPlEqlSb4btm7dirCwMHzyySfStvz8fOTk5DzV8WrVqoUuXbpI9/fv3y9N+ng0r4dbRX/88Udotdpyz33jxo1wcnLCSy+9VO6xd+7ciQkTJmDQoEFlfm8/rb/++gtarRavv/46Xn/99VL7fX198cYbb+jNPDNmTrdu3cKDBw/K/EOgqKgIWq22zH3fffcdhBCPLSYfJoTA1atX8c9//vOpcjYGdpmZ2f3797Ft2zb07dsXQ4YMKXWbMmUK8vLypKnywcHBKCoqwpo1a6Tn0Gq1pf4DuLm5oUePHvjyyy+lvvWHPTydvjIcHR2f+ovsYZ6enmjdujXWrVun97xJSUn4+eefpSbf4uLiUs3rbm5u8PLyKjUl/mG6otGQnHXHenR669KlSwEAoaGhj30OQ1lZWWHIkCGIiYnB+vXr8eDBA73uMqDkS+lhNjY2UKvVEELozWIqT+fOndGgQQNs2LABW7ZsQffu3VGvXj1pv7W1NRQKhd4X29WrVw1abVj3oxofHy9t0027flhwcDCUSiU+/PDDMnN+3OfQ2tq6VCvEDz/8UGr8m65oftrPZqNGjXDhwgW9vP744w+Duyof9fDn++HPb2xsLJKTk/Vin+b9qIy2bduiUaNG+Pjjj6Up6g8z9ndDWe/h8uXLy211exJbtmzBiRMnMG3aNL0WjUfdv38fc+fOhaenp94fBzo3b97E/v37MXDgwFLDEnTi4+MxYsQIdOvWDRs3bqzweIb6+++/ceHCBWnWnW5phkdvLVq0QP369bF9+3aMHz++ynJyc3ODi4sLtm/frjcb9s6dO4iJiUGzZs3KnOm3adMm1K9fX69QfVhZn61Vq1bh5s2bCAkJeaqcjYEtRGa2a9cu5OXllfvXSMeOHVG3bl1s3LgRw4cPx4ABA9C+fXu8+eabuHz5Mpo1a4Zdu3ZJzY0P/5W2YsUKdOnSBf7+/pg4cSIaNmyIzMxMJCQk4MaNG3pruRiqbdu2WLVqFd5//300btwYbm5u6NWr15Od/P8sWbIEffr0QUBAAMaPH4/79+9j+fLlUKlU0ppBeXl5qFevHoYMGYJWrVrByckJ+/fvx4kTJ/T+8nyUvb091Go1tmzZgueeew6urq7w8/Mrtfw9ALRq1QphYWH46quvkJOTg+7du+P48eNYt24dBgwYIA14N5bhw4dj+fLliIqKgr+/v16rFAAEBQXBw8MDnTt3hru7O86fP48vvvgCoaGheoNmy6NbQ+XDDz8EAGlquk5oaCiWLl2KkJAQjBw5EllZWVixYgUaN26MM2fOVPjcQUFBqF+/PsaPH49Zs2bB2toa33zzDerWrYvU1FQpTqlUYtWqVRgzZgzatGmDESNGSDF79uxB586d8cUXX5R7nL59++Ldd9/FuHHj0KlTJ5w9exYbN26UWkB1GjVqBBcXF6xevRrOzs5wdHREhw4d4Ovr+9jX6WGvvPIKli5diuDgYIwfPx5ZWVlYvXo1WrRoYdAA4rIsXLgQoaGh6NKlC1555RVkZ2dL60s9XJA8zftRGVZWVvjPf/6DPn36oEWLFhg3bhz+8Y9/4K+//sKhQ4egVCoRExNT6edt27Yt9u/fj6VLl8LLywu+vr7o0KED+vbti/Xr10OlUkGtViMhIQH79+9/7Pic8sTHx+Pdd99FUFAQateujaNHj2Lt2rUICQkpNbZl2LBh8PLyglqthkajwTfffIM///wTe/bsKfP/0JYtW/DgwYNyWziuXbuGl156CQqFAkOGDMEPP/ygt79ly5Zo2bKldH/9+vW4du2aVOjEx8fj/fffBwCMGTNGakH84osvsGDBAhw6dAg9evRAnTp1MGDAgFLH1/2x9vC+qsjJ2toaM2fOxJw5c9CxY0eMHTsWxcXF+Prrr3Hjxg1s2LChVG5JSUk4c+YM3n777XJbC318fDB8+HD4+/vDzs4Ov/76KzZv3ozWrVtLyzSYlXkmt5FOv379hJ2dnbh79265MeHh4aJmzZrSyqw3b94UI0eOFM7OzkKlUonw8HDx22+/CQBi8+bNeo+9cuWKGDt2rPDw8BA1a9YU//jHP0Tfvn2lqalC/N9U6RMnTug9tqxpsBkZGSI0NFQ4OzsLAI+dgo8yVqouy/79+0Xnzp2Fvb29UCqVol+/fiI5OVnaX1BQIGbNmiVatWolnJ2dhaOjo2jVqlWpabFlrf575MgR0bZtW2FjY6M3zbSsqeNFRUViwYIFwtfXV9SsWVN4e3uLyMjIUqs++/j4iNDQ0FLnUd607bJotVrh7e0tAIj333+/1P4vv/xSdOvWTdSuXVvY2tqKRo0aiVmzZlVqeuq5c+ekKdq3b98utf/rr78WTZo0Eba2tqJZs2Zi7dq1Zb4uZa1UferUKdGhQwdhY2Mj6tevL5YuXVpq2r3OoUOHRHBwsFCpVMLOzk40atRIhIeHi5MnT1aYf35+vnjzzTeFp6ensLe3F507dxYJCQllvs47d+4UarVa1KhR47FT8Mubdi+EEBs2bBANGzYUNjY2onXr1mLfvn3lTrtfsmRJqcejjKnM//3vf0Xz5s2Fra2tUKvVYtu2bWV+Vg19P8r6f1VeTuWd6++//y4GDRokfb58fHzEsGHDxIEDB6SY8lYWLut9vnDhgujWrZuwt7cXAKTPy+3bt8W4ceNEnTp1hJOTkwgODhYXLlwo9ZkydNr95cuXRVBQkKhTp470Oi1cuFAUFBSUil20aJFo1qyZsLOzE7Vq1RIvvfSS+P3338t97o4dOwo3Nze9Kf0P0+VY3u3R97179+7lxj58nrrX+XHnXta0+6rKSQghNm7cKNq3by9cXFyEvb296NChg95vx8PefvttAUCcOXOm3PwnTJgg1Gq1cHZ2FjVr1hSNGzcWs2fP1lsmwpwUQljYUpH0RHbs2IGBAwfi119/lRbdIyIiIsOwIKqG7t+/r9d/W1xcjKCgIJw8eRIZGRmPXVadiIiI9HEMUTU0depU3L9/HwEBASgoKMC2bdtw5MgRfPjhhyyGiIiIngBbiKqhTZs24ZNPPsHly5eRn5+Pxo0bY/LkyXrXHSMiIiLDsSAiIiIi2eM6RERERCR7LIiIiIhI9jio2gBarRZpaWlwdnY2+qUriIiIqGoIIZCXlwcvL6/HruDNgsgAaWlppa4gTURERNXD9evX9S5dVBYWRAbQLfF+/fp1KJVKM2dDREREhtBoNPD29jbockcsiAyg6yZTKpUsiIiIiKoZQ4a7cFA1ERERyR4LIiIiIpI9FkREREQkeyyIiIiISPZYEBEREZHssSAiIiIi2WNBRERERLLHgoiIiIhkjwURERERyR5XqjajYq3A8ZRsZOXlw83ZDu19XWFtxYvHEhERmZpZW4hWrVqFli1bSpfECAgIwE8//STt79GjBxQKhd5t0qRJes+RmpqK0NBQODg4wM3NDbNmzcKDBw/0YuLi4tCmTRvY2tqicePGiI6ONsXpVWhvUjq6LDqIl9ccxRubE/HymqPosugg9ialmzs1IiIi2TFrQVSvXj189NFHOHXqFE6ePIlevXqhf//+OHfunBQzceJEpKenS7fFixdL+4qLixEaGorCwkIcOXIE69atQ3R0NObNmyfFpKSkIDQ0FD179kRiYiKmTZuGCRMmYN++fSY914ftTUrH5A2nkZ6br7c9IzcfkzecZlFERERkYgohhDB3Eg9zdXXFkiVLMH78ePTo0QOtW7fGsmXLyoz96aef0LdvX6SlpcHd3R0AsHr1asyePRs3b96EjY0NZs+ejT179iApKUl63IgRI5CTk4O9e/calJNGo4FKpUJubu5TX9y1WCvQZdHBUsWQjgKAh8oOv87uxe4zIiKip1CZ32+LGVRdXFyMzZs34+7duwgICJC2b9y4EXXq1IGfnx8iIyNx7949aV9CQgL8/f2lYggAgoODodFopFamhIQEBAYG6h0rODgYCQkJ5eZSUFAAjUajdzOW4ynZ5RZDACAApOfm43hKttGOSURERBUz+6Dqs2fPIiAgAPn5+XBycsL27duhVqsBACNHjoSPjw+8vLxw5swZzJ49GxcvXsS2bdsAABkZGXrFEADpfkZGRoUxGo0G9+/fh729famcFi5ciAULFhj9XAEgK6/8YuhJ4oiIiOjpmb0gatq0KRITE5Gbm4utW7ciLCwMhw8fhlqtxquvvirF+fv7w9PTEy+88AKuXLmCRo0aVVlOkZGRmDFjhnRfo9HA29vbKM/t5mxn1DgiIiJ6embvMrOxsUHjxo3Rtm1bLFy4EK1atcJnn31WZmyHDh0AAJcvXwYAeHh4IDMzUy9Gd9/Dw6PCGKVSWWbrEADY2tpKM990N2Np7+sKT5UdyhsdpADgqSqZgk9ERESmYfaC6FFarRYFBQVl7ktMTAQAeHp6AgACAgJw9uxZZGVlSTGxsbFQKpVSt1tAQAAOHDig9zyxsbF645RMydpKgah+Jbk9WhTp7kf1U3NANRERkQmZtSCKjIxEfHw8rl69irNnzyIyMhJxcXEYNWoUrly5gvfeew+nTp3C1atXsWvXLowdOxbdunVDy5YtAQBBQUFQq9UYM2YM/vjjD+zbtw9z5sxBREQEbG1tAQCTJk3Cn3/+ibfeegsXLlzAypUr8f3332P69OlmO+8QP0+sGt0GHir9bjEPlR1WjW6DED9PM2VGREQkT2addj9+/HgcOHAA6enpUKlUaNmyJWbPno3evXvj+vXrGD16NJKSknD37l14e3tj4MCBmDNnjl4X1rVr1zB58mTExcXB0dERYWFh+Oijj1Cjxv8Nj4qLi8P06dORnJyMevXqYe7cuQgPDzc4T2NOu38YV6omIiKqOpX5/ba4dYgsUVUVRERERFR1quU6RERERETmwoKIiIiIZI8FEREREckeCyIiIiKSPRZEREREJHssiIiIiEj2WBARERGR7LEgIiIiItljQURERESyx4KIiIiIZI8FEREREckeCyIiIiKSPRZEREREJHssiIiIiEj2WBARERGR7LEgIiIiItljQURERESyx4KIiIiIZI8FEREREckeCyIiIiKSPRZEREREJHssiIiIiEj2WBARERGR7LEgIiIiItljQURERESyx4KIiIiIZI8FEREREckeCyIiIiKSPRZEREREJHssiIiIiEj2WBARERGR7LEgIiIiItljQURERESyx4KIiIiIZI8FEREREckeCyIiIiKSPbMWRKtWrULLli2hVCqhVCoREBCAn376Sdqfn5+PiIgI1K5dG05OThg8eDAyMzP1niM1NRWhoaFwcHCAm5sbZs2ahQcPHujFxMXFoU2bNrC1tUXjxo0RHR1titMjIiKiasKsBVG9evXw0Ucf4dSpUzh58iR69eqF/v3749y5cwCA6dOnIyYmBj/88AMOHz6MtLQ0DBo0SHp8cXExQkNDUVhYiCNHjmDdunWIjo7GvHnzpJiUlBSEhoaiZ8+eSExMxLRp0zBhwgTs27fP5OdLRERElkkhhBDmTuJhrq6uWLJkCYYMGYK6deti06ZNGDJkCADgwoULaN68ORISEtCxY0f89NNP6Nu3L9LS0uDu7g4AWL16NWbPno2bN2/CxsYGs2fPxp49e5CUlCQdY8SIEcjJycHevXsNykmj0UClUiE3NxdKpdL4J01ERERGV5nfb4sZQ1RcXIzNmzfj7t27CAgIwKlTp1BUVITAwEApplmzZqhfvz4SEhIAAAkJCfD395eKIQAIDg6GRqORWpkSEhL0nkMXo3uOshQUFECj0ejdiIiI6Nll9oLo7NmzcHJygq2tLSZNmoTt27dDrVYjIyMDNjY2cHFx0Yt3d3dHRkYGACAjI0OvGNLt1+2rKEaj0eD+/ftl5rRw4UKoVCrp5u3tbYxTJSIiIgtl9oKoadOmSExMxLFjxzB58mSEhYUhOTnZrDlFRkYiNzdXul2/ft2s+RAREVHVqmHuBGxsbNC4cWMAQNu2bXHixAl89tlnGD58OAoLC5GTk6PXSpSZmQkPDw8AgIeHB44fP673fLpZaA/HPDozLTMzE0qlEvb29mXmZGtrC1tbW6OcHxEREVk+s7cQPUqr1aKgoABt27ZFzZo1ceDAAWnfxYsXkZqaioCAAABAQEAAzp49i6ysLCkmNjYWSqUSarVainn4OXQxuucgIiIiMmsLUWRkJPr06YP69esjLy8PmzZtQlxcHPbt2weVSoXx48djxowZcHV1hVKpxNSpUxEQEICOHTsCAIKCgqBWqzFmzBgsXrwYGRkZmDNnDiIiIqQWnkmTJuGLL77AW2+9hVdeeQUHDx7E999/jz179pjz1ImIiMiCmLUgysrKwtixY5Geng6VSoWWLVti37596N27NwDg008/hZWVFQYPHoyCggIEBwdj5cqV0uOtra2xe/duTJ48GQEBAXB0dERYWBjeffddKcbX1xd79uzB9OnT8dlnn6FevXr4z3/+g+DgYJOfLxEREVkmi1uHyBJxHSIiIqLqp1quQ0RERERkLiyIiIiISPZYEBEREZHssSAiIiIi2WNBRERERLLHgoiIiIhkjwURERERyR4LIiIiIpI9FkREREQkeyyIiIiISPZYEBEREZHssSAiIiIi2WNBRERERLLHgoiIiIhkjwURERERyR4LIiIiIpI9FkREREQkeyyIiIiISPZYEBEREZHssSAiIiIi2WNBRERERLLHgoiIiIhkjwURERERyR4LIiIiIpI9FkREREQkeyyIiIiISPZYEBEREZHssSAiIiIi2WNBRERERLLHgoiIiIhkjwURERERyR4LIiIiIpI9FkREREQkeyyIiIiISPZYEBEREZHssSAiIiIi2TNrQbRw4UI8//zzcHZ2hpubGwYMGICLFy/qxfTo0QMKhULvNmnSJL2Y1NRUhIaGwsHBAW5ubpg1axYePHigFxMXF4c2bdrA1tYWjRs3RnR0dFWfHhEREVUTZi2IDh8+jIiICBw9ehSxsbEoKipCUFAQ7t69qxc3ceJEpKenS7fFixdL+4qLixEaGorCwkIcOXIE69atQ3R0NObNmyfFpKSkIDQ0FD179kRiYiKmTZuGCRMmYN++fSY7VyIiIrJcCiGEMHcSOjdv3oSbmxsOHz6Mbt26AShpIWrdujWWLVtW5mN++ukn9O3bF2lpaXB3dwcArF69GrNnz8bNmzdhY2OD2bNnY8+ePUhKSpIeN2LECOTk5GDv3r2PzUuj0UClUiE3NxdKpfLpT5SIiIiqXGV+vy1qDFFubi4AwNXVVW/7xo0bUadOHfj5+SEyMhL37t2T9iUkJMDf318qhgAgODgYGo0G586dk2ICAwP1njM4OBgJCQll5lFQUACNRqN3IyIiomdXDXMnoKPVajFt2jR07twZfn5+0vaRI0fCx8cHXl5eOHPmDGbPno2LFy9i27ZtAICMjAy9YgiAdD8jI6PCGI1Gg/v378Pe3l5v38KFC7FgwQKjnyMRERFZJospiCIiIpCUlIRff/1Vb/urr74q/dvf3x+enp544YUXcOXKFTRq1KhKcomMjMSMGTOk+xqNBt7e3lVyLCIiIjI/i+gymzJlCnbv3o1Dhw6hXr16FcZ26NABAHD58mUAgIeHBzIzM/VidPc9PDwqjFEqlaVahwDA1tYWSqVS70ZERETPLrMWREIITJkyBdu3b8fBgwfh6+v72MckJiYCADw9PQEAAQEBOHv2LLKysqSY2NhYKJVKqNVqKebAgQN6zxMbG4uAgAAjnQkRERFVZ2YtiCIiIrBhwwZs2rQJzs7OyMjIQEZGBu7fvw8AuHLlCt577z2cOnUKV69exa5duzB27Fh069YNLVu2BAAEBQVBrVZjzJgx+OOPP7Bv3z7MmTMHERERsLW1BQBMmjQJf/75J9566y1cuHABK1euxPfff4/p06eb7dyJiIjIcph12r1CoShz+9q1axEeHo7r169j9OjRSEpKwt27d+Ht7Y2BAwdizpw5et1Y165dw+TJkxEXFwdHR0eEhYXho48+Qo0a/zdEKi4uDtOnT0dycjLq1auHuXPnIjw83KA8Oe2eiIio+qnM77dFrUNkqVgQERERVT/Vdh0iIiIiInNgQURERESyx4KIiIiIZI8FEREREckeCyIiIiKSPRZEREREJHssiIiIiEj2WBARERGR7LEgIiIiItljQURERESyx4KIiIiIZI8FEREREckeCyIiIiKSPRZEREREJHssiIiIiEj2WBARERGR7LEgIiIiItljQURERESyx4KIiIiIZI8FEREREckeCyIiIiKSPRZEREREJHssiIiIiEj2WBARERGR7LEgIiIiItljQURERESyx4KIiIiIZI8FEREREckeCyIiIiKSPRZEREREJHssiIiIiEj2WBARERGR7LEgIiIiItmrYe4ESL6KtQLHU7KRlZcPN2c7tPd1hbWVwtxpERGRDLEgIrPYm5SOBTHJSM/Nl7Z5quwQ1U+NED9PM2ZGRERyZNYus4ULF+L555+Hs7Mz3NzcMGDAAFy8eFEvJj8/HxEREahduzacnJwwePBgZGZm6sWkpqYiNDQUDg4OcHNzw6xZs/DgwQO9mLi4OLRp0wa2trZo3LgxoqOjq/r0qBx7k9IxecNpvWIIADJy8zF5w2nsTUo3U2ZERCRXZi2IDh8+jIiICBw9ehSxsbEoKipCUFAQ7t69K8VMnz4dMTEx+OGHH3D48GGkpaVh0KBB0v7i4mKEhoaisLAQR44cwbp16xAdHY158+ZJMSkpKQgNDUXPnj2RmJiIadOmYcKECdi3b59Jz5dKuskWxCRDlLFPt21BTDKKtWVFEBERVQ2FEMJifnlu3rwJNzc3HD58GN26dUNubi7q1q2LTZs2YciQIQCACxcuoHnz5khISEDHjh3x008/oW/fvkhLS4O7uzsAYPXq1Zg9ezZu3rwJGxsbzJ49G3v27EFSUpJ0rBEjRiAnJwd79+59bF4ajQYqlQq5ublQKpVVc/IykXDlFl5ec/Sxcd9N7IiARrVNkBERET2rKvP7bVGzzHJzcwEArq6uAIBTp06hqKgIgYGBUkyzZs1Qv359JCQkAAASEhLg7+8vFUMAEBwcDI1Gg3PnzkkxDz+HLkb3HGQ6WXn5jw+qRBwREZExWMygaq1Wi2nTpqFz587w8/MDAGRkZMDGxgYuLi56se7u7sjIyJBiHi6GdPt1+yqK0Wg0uH//Puzt7fX2FRQUoKCgQLqv0Wie/gQJAODmbGfUOCIiImOwmBaiiIgIJCUlYfPmzeZOBQsXLoRKpZJu3t7e5k7pmdHe1xWeKjuUN7legZLZZu19XU2ZFhERyZxFFERTpkzB7t27cejQIdSrV0/a7uHhgcLCQuTk5OjFZ2ZmwsPDQ4p5dNaZ7v7jYpRKZanWIQCIjIxEbm6udLt+/fpTnyOVsLZSIKqfGgBKFUW6+1H91FyPiIiITMqsBZEQAlOmTMH27dtx8OBB+Pr66u1v27YtatasiQMHDkjbLl68iNTUVAQEBAAAAgICcPbsWWRlZUkxsbGxUCqVUKvVUszDz6GL0T3Ho2xtbaFUKvVuZDwhfp5YNboNPFT63WIeKjusGt2G6xAREZHJmXWW2WuvvYZNmzZh586daNq0qbRdpVJJLTeTJ0/Gjz/+iOjoaCiVSkydOhUAcOTIEQAl0+5bt24NLy8vLF68GBkZGRgzZgwmTJiADz/8EEDJtHs/Pz9ERETglVdewcGDB/H6669jz549CA4OfmyenGVWNbhSNRERVaXK/H6btSBSKMr+8Vu7di3Cw8MBlCzM+Oabb+K7775DQUEBgoODsXLlSqk7DACuXbuGyZMnIy4uDo6OjggLC8NHH32EGjX+b8x4XFwcpk+fjuTkZNSrVw9z586VjvE4LIiIiIiqn2pTEFUXLIiIiIiqn2q7DhERERGRObAgIiIiItmzmIUZSX44qJqIiCwFCyIyi71J6VgQk6x3xXtPlR2i+qk57Z6IiEzuibrMHjx4gP379+PLL79EXl4eACAtLQ137twxanL0bNqblI7JG07rFUMAkJGbj8kbTmNvUrqZMiMiIrmqdAvRtWvXEBISgtTUVBQUFKB3795wdnbGokWLUFBQgNWrV1dFnvSMKNYKLIhJRllTGwVKVqteEJOM3moPdp8REZHJVLqF6I033kC7du1w+/ZtvcteDBw4sNRq0ESPOp6SXapl6GECQHpuPo6nZJsuKSIikr1KtxD98ssvOHLkCGxsbPS2N2jQAH/99ZfREqNnU1Ze+cXQk8QREREZQ6VbiLRaLYqLi0ttv3HjBpydnY2SFD273JztHh9UiTgiIiJjqHRBFBQUhGXLlkn3FQoF7ty5g6ioKLz44ovGzI2eQe19XeGpsit1pXsdBUpmm7X3dTVlWmZTrBVIuHILOxP/QsKVWyjWcuF4IiJzqPSlO27cuIHg4GAIIXDp0iW0a9cOly5dQp06dRAfHw83N7eqytVseOkO49LNMgOgN7haVyTJ5Yr3XHqAiKhqVfm1zB48eIDNmzfjzJkzuHPnDtq0aYNRo0bpDbJ+lrAgMj65FwO6ovDR/3xyKwqJiKoSL+5qZCyIqoZcV6ou1gp0WXSw3Nl2CgAeKjv8OruXLF4PIqKqUpnf70rPMvv2228r3D927NjKPiXJlLWVAgGNaps7DZOrzNIDcnx9iIjModIF0RtvvKF3v6ioCPfu3YONjQ0cHBxYEBE9BpceICKyPJWeZXb79m292507d3Dx4kV06dIF3333XVXkSPRMcbW3eXxQJeKIiOjpPdG1zB7VpEkTfPTRR6Vaj4iotAuZeUaNIyKip2eUgggAatSogbS0NGM9HdEz6/rte0aNIyKip1fpMUS7du3Suy+EQHp6Or744gt07tzZaIkRPat8XB2MGkdERE+v0gXRgAED9O4rFArUrVsXvXr1wieffGKsvIieWWMCGuCDH8+jokWprRQlcUREZBqVLoi0Wm1V5EEkGzY1rDCxqy++jE8pN2ZiV1/Y1DBajzYRET0Gv3GJzCDyRTV6q8u+zE1vtRsiX1SbOCMiInkzqIVoxowZBj/h0qVLnzgZIrnYm5SO/clZZe7bn5yFvUnpvHQHEZEJGVQQ/f777wY9mULBywwQPU6xVmBBTHKp65g9bEFMMnqrPXjpDiIiEzGoIDp06FBV50EkG7x0BxGR5eEYIiIT46U7iIgsT6VnmQHAyZMn8f333yM1NRWFhYV6+7Zt22aUxIieVW7OdkaNIyKip1fpFqLNmzejU6dOOH/+PLZv346ioiKcO3cOBw8ehEqlqoociZ4p7X1d4amyQ3mjgxQAPFV2aO/rasq0iIhkrdIF0YcffohPP/0UMTExsLGxwWeffYYLFy5g2LBhqF+/flXkSPRMsbZSIKpfybT6R4si3f2ofmoOqCYiMqFKF0RXrlxBaGgoAMDGxgZ3796FQqHA9OnT8dVXXxk9QaJnUYifJ1aNbgMPlX63mIfKDqtGt+GUeyIiE6v0GKJatWohL6/kKtz/+Mc/kJSUBH9/f+Tk5ODePV6MkshQIX6e6K32wPGUbGTl5cPNuaSbjC1DRESmZ3BBlJSUBD8/P3Tr1g2xsbHw9/fH0KFD8cYbb+DgwYOIjY3FCy+8UJW5Ej1zrK0UnFpPRGQBDC6IWrZsieeffx4DBgzA0KFDAQD//ve/UbNmTRw5cgSDBw/GnDlzqixRIiIioqqiEEJUtGCu5JdffsHatWuxdetWaLVaDB48GBMmTEDXrl2rOkez02g0UKlUyM3NhVKpNHc6REREZIDK/H4bPKi6a9eu+Oabb5Ceno7ly5fj6tWr6N69O5577jksWrQIGRkZT504ERERkTkY3EJUlsuXL2Pt2rVYv349MjIyEBISgl27dhkzP4vAFiKqKsVawUHVRERVpEpaiMrSuHFjvPPOO5gzZw6cnZ2xZ8+eSj0+Pj4e/fr1g5eXFxQKBXbs2KG3Pzw8HAqFQu8WEhKiF5OdnY1Ro0ZBqVTCxcUF48ePx507d/Rizpw5g65du8LOzg7e3t5YvHjxE50vkTHtTUpHl0UH8fKao3hjcyJeXnMUXRYdxN6kdHOnRkQkO09cEMXHxyM8PBweHh6YNWsWBg0ahN9++61Sz3H37l20atUKK1asKDcmJCQE6enp0u27777T2z9q1CicO3cOsbGx2L17N+Lj4/Hqq69K+zUaDYKCguDj44NTp05hyZIlmD9/PtdMIrPam5SOyRtOl7rIa0ZuPiZvOM2iiIjIxCq1DlFaWhqio6MRHR2Ny5cvo1OnTvj8888xbNgwODo6Vvrgffr0QZ8+fSqMsbW1hYeHR5n7zp8/j7179+LEiRNo164dAGD58uV48cUX8fHHH8PLywsbN25EYWEhvvnmG9jY2KBFixZITEzE0qVL9QonIlMp1gosiElGWX3VAiWrVS+ISUZvtQe7z4iITMTgFqI+ffrAx8cHy5cvx8CBA3H+/Hn8+uuvGDdu3BMVQ4aKi4uDm5sbmjZtismTJ+PWrVvSvoSEBLi4uEjFEAAEBgbCysoKx44dk2K6desGGxsbKSY4OBgXL17E7du3qyxvovIcT8ku1TL0MAEgPTcfx1OyTZcUEZHMGdxCVLNmTWzduhV9+/aFtbV1VeYkCQkJwaBBg+Dr64srV67gnXfeQZ8+fZCQkABra2tkZGTAzc1N7zE1atSAq6urNOstIyMDvr6+ejHu7u7Svlq1apU6bkFBAQoKCqT7Go3G2KdGMpaVV34x9CRxRET09AwuiMwxe2zEiBHSv/39/dGyZUs0atQIcXFxVboq9sKFC7FgwYIqe36SNzdnu8cHVSKOiIie3lPNMjO1hg0bok6dOrh8+TIAwMPDA1lZWXoxDx48QHZ2tjTuyMPDA5mZmXoxuvvljU2KjIxEbm6udLt+/bqxT4VkrL2vKzxVdqWudK+jAOCpKpmCT0REplGtCqIbN27g1q1b8PQsuRJ4QEAAcnJycOrUKSnm4MGD0Gq16NChgxQTHx+PoqIiKSY2NhZNmzYts7sMKBnIrVQq9W5ExmJtpUBUPzUAlCqKdPej+qk5oJqIyITMWhDduXMHiYmJSExMBACkpKQgMTERqampuHPnDmbNmoWjR4/i6tWrOHDgAPr374/GjRsjODgYANC8eXOEhIRg4sSJOH78OH777TdMmTIFI0aMgJeXFwBg5MiRsLGxwfjx43Hu3Dls2bIFn332GWbMmGGu0yZCiJ8nVo1uAw+VfreYh8oOq0a3QYifp5kyIyKSp6daqfppxcXFoWfPnqW2h4WFYdWqVRgwYAB+//135OTkwMvLC0FBQXjvvfekQdFAycKMU6ZMQUxMDKysrDB48GB8/vnncHJykmLOnDmDiIgInDhxAnXq1MHUqVMxe/Zsg/PkStVUVQofaLE+4SquZd+Dj6sDxgQ0gE2NatVwS0RksSrz+23Wgqi6YEFEVWFvUjoWxCTrTcH3VNkhqp+aLUREREZgskt3ENGTKW+l6nSuVE1EZBYsiIhMrKKVqoGShRkXxCSjWMvGWyIiU2FBRGRij1upGuBK1UREpsaCiMjEMnLvGzWOiIieHgsiIhPLvlto1DgiInp6LIiITMzVydaocURE9PRYEBGZmIfSsGuUGRpHRERPjwURkYnprmVWEV7LjIjItFgQEZmYtZUCL7WqeOHFl1p58lpmREQmxIKIyMSKtQK7/qh44cVdf6RzHSIiIhNiQURkYlyHiIjI8rAgIjKxrLyKi6HKxhER0dOrYe4EiOTGzdmw2WOGxhFR9VWsFTieko2svHy4OZdMpuD4QfNgQURkYrpZZhm5+WVez0wBwIOzzIieeXuT0rEgJlmvC91TZYeofmqE+FU88YKMj11mRCZmbaVAVD81gJLi52G6+1H91PwrkegZtjcpHZM3nC41njAjNx+TN5zG3qSKJ16Q8bEgIjKDED9PrBrdBh6PrEfkobLDqtFt+Nch0TOsWCuwICa5zBZi3bYFMcmcaWpi7DIjMpMQP0/0Vntw/ACRzDxupqnA/800DWhU23SJyRwLIiIzsrZS8AuPSGY409QyscuMiIjIhDjT1DKxICIiIjIh3UzT8jrHFeD1DM2BBREREZEJcaapZWJBREREZGKcaWp5OKiaiIjIDDjT1LKwICIiIjITzjS1HOwyIyIiItljQURERESyx4KIiIiIZI8FEREREckeCyIiIiKSPRZEREREJHssiIiIiEj2WBARERGR7LEgIiIiItljQURERESyx4KIiIiIZI/XMiMiIiKzKdYKi7jArVlbiOLj49GvXz94eXlBoVBgx44devuFEJg3bx48PT1hb2+PwMBAXLp0SS8mOzsbo0aNglKphIuLC8aPH487d+7oxZw5cwZdu3aFnZ0dvL29sXjx4qo+NSIiInqMvUnp6LLoIF5ecxRvbE7Ey2uOosuig9iblG7yXMxaEN29exetWrXCihUryty/ePFifP7551i9ejWOHTsGR0dHBAcHIz8/X4oZNWoUzp07h9jYWOzevRvx8fF49dVXpf0ajQZBQUHw8fHBqVOnsGTJEsyfPx9fffVVlZ8fERERlW1vUjombziN9Nx8ve0ZufmYvOG0yYsihRBCmPSI5VAoFNi+fTsGDBgAoKR1yMvLC2+++SZmzpwJAMjNzYW7uzuio6MxYsQInD9/Hmq1GidOnEC7du0AAHv37sWLL76IGzduwMvLC6tWrcK///1vZGRkwMbGBgDw9ttvY8eOHbhw4YJBuWk0GqhUKuTm5kKpVBr/5ImISJYspbvI1Iq1Al0WHSxVDOkoAHio7PDr7F5P9XpU5vfbYgdVp6SkICMjA4GBgdI2lUqFDh06ICEhAQCQkJAAFxcXqRgCgMDAQFhZWeHYsWNSTLdu3aRiCACCg4Nx8eJF3L59u8xjFxQUQKPR6N2IiIiMyZK6i0zteEp2ucUQAAgA6bn5OJ6SbbKcLLYgysjIAAC4u7vrbXd3d5f2ZWRkwM3NTW9/jRo14OrqqhdT1nM8fIxHLVy4ECqVSrp5e3s//QkRERH9j6V1F5laVl75xdCTxBmDxRZE5hQZGYnc3Fzpdv36dXOnREREz4hircCCmGSUNV5Ft21BTDKKtRYxoqVKuDnbGTXOGCy2IPLw8AAAZGZm6m3PzMyU9nl4eCArK0tv/4MHD5Cdna0XU9ZzPHyMR9na2kKpVOrdiIiIjMESu4tMrb2vKzxVdihvdJACgKeqZEyVqVhsQeTr6wsPDw8cOHBA2qbRaHDs2DEEBAQAAAICApCTk4NTp05JMQcPHoRWq0WHDh2kmPj4eBQVFUkxsbGxaNq0KWrVqmWisyEiIiphid1FpmZtpUBUPzUAlCqKdPej+qlNOsDcrAXRnTt3kJiYiMTERAAlA6kTExORmpoKhUKBadOm4f3338euXbtw9uxZjB07Fl5eXtJMtObNmyMkJAQTJ07E8ePH8dtvv2HKlCkYMWIEvLy8AAAjR46EjY0Nxo8fj3PnzmHLli347LPPMGPGDDOdNRERyZkldheZQ4ifJ1aNbgMPlf55eqjssGp0G4T4eZo0H7NOu4+Li0PPnj1LbQ8LC0N0dDSEEIiKisJXX32FnJwcdOnSBStXrsRzzz0nxWZnZ2PKlCmIiYmBlZUVBg8ejM8//xxOTk5SzJkzZxAREYETJ06gTp06mDp1KmbPnm1wnpx2T0RExqKbcp6Rm1/mOCJjTTmvLqpy6YHK/H5bzDpElowFERERGZNulhkAvaJIVwaYo4XkWfRMrENERET0rLK07iLixV2JyIzkukovEVBSFPVWe/D/gIVgQUREZrE3KR0LYpL1ph97quwQ1U/Nv45JNqytFAhoVNvcaRDYZUZEZiD3VXqJyPKwICIik+IqvURkiVgQEZFJcZVeIrJELIiIyKS4Si8RWSIWRERkUlyll4gsEQsiIjKptj618LhZxVaKkjgiIlNhQUREJnXq2m08bry0VpTEERGZCgsiIjIpjiEiIkvEgoiITIpjiIjIErEgIiKTau/rCk+VHcobRqRAyYrV7X1dTZkWEckcCyIiMilrKwWi+qkrjInqp+b1nIjIpFgQEZHJhfh5wr+essx9/vWUvJYZEZkcCyIiMrmJ357AmRuaMveduaHBxG9PmDgjIvMo1gokXLmFnYl/IeHKLV6yxox4tXsiMqn7hcWITc6qMCY2OQv3C4thb2NtoqyITG9vUjoWxCTrXcrGU2WHqH5qtpKaAVuIiMikPvwx2ahxRNXR3qR0TN5wutR1/TJy8zF5w2nsTUo3U2byxYKIiEzq6q17Ro0jqm6KtQILYpJRVueYbtuCmGR2n5kYCyIiMinvWvZGjSOqbo6nZJdqGXqYAJCem4/jKdmmS4pYEBGRadVzMazQMTSOqLrhau2WiQUREZlU4o1co8YRVTdcrd0ycZYZEZmUg4EzxwyNo+qtWCtwPCUbWXn5cHMuWaH8WV+UU7dae0ZufpnjiBQAPLhau8mxICIyIzn+GAz+Zz3sSEwzKI6ebXKddq5brX3yhtNQAHpFke5/P1drNz0WRERmItcfgw6Nahs1jqon3bTzR1tIdNPOV41u80z/Pwjx88Sq0W1KfQd4yOA7wFKxICIyAzn/GJwwcObMiZRsdG5Sp4qzIXN43LRzBUqmnfdWezzTrSQhfp7orfaQXSuxpeKgaiITk/saJAl//m3UOKp+OO38/1hbKRDQqDb6t/4HAhrVZjFkRiyIiExM7j8GhhZ6z2pBSJx2TpaJBRGRicn9xyDnXpFR46j64bRzskQsiIhMTO4/Bpcy84waR9WPbtp5eZ1DCpRMMOC0czIlFkREJib3HwNDO8LYYfbs0k07rwinnctHsVYg4cot7Ez8CwlXbpmtu5yzzIhMTO5rkDzn7oRTqTkGxdGzK8TPE69288WaX1Lw8O+flQKY2NX3mZ1lSfosafkRthARmYFuDRIPlX63mIfK7pmecg8AKgcbo8ZR9bQ3KR1fxesXQwAgBPBVfAr2JqWbJzEyGd3yI49OMtEtP2LqzwBbiIjMRK5rkNQw8PwMjaPqh+sQkSV+BthCRGRGclyDpEMDA1eqNjCOqh+5Lz1BlvkZYEFERKZlaM337NeGsiX3pSfIMj8DFl0QzZ8/HwqFQu/WrFkzaX9+fj4iIiJQu3ZtODk5YfDgwcjMzNR7jtTUVISGhsLBwQFubm6YNWsWHjx4YOpTIaL/OZZyy6hxVP3IfekJsszPgEUXRADQokULpKenS7dff/1V2jd9+nTExMTghx9+wOHDh5GWloZBgwZJ+4uLixEaGorCwkIcOXIE69atQ3R0NObNm2eOUyEiAGwiIrkvPUGW+Rmw+IKoRo0a8PDwkG516pRc7DE3Nxdff/01li5dil69eqFt27ZYu3Ytjhw5gqNHjwIAfv75ZyQnJ2PDhg1o3bo1+vTpg/feew8rVqxAYWGhOU+LSLYCDLyKvaFxVP08vA7Roz+Iclh64mGWsgaPqVniZ8DiC6JLly7By8sLDRs2xKhRo5CamgoAOHXqFIqKihAYGCjFNmvWDPXr10dCQgIAICEhAf7+/nB3d5digoODodFocO7cuXKPWVBQAI1Go3cjIuPo2LA2XBxqVhhTy6EmOjZkQfQs0y094a6U39ITOnuT0tFl0UG8vOYo3ticiJfXHEWXRQdls+SApS0/YtHT7jt06IDo6Gg0bdoU6enpWLBgAbp27YqkpCRkZGTAxsYGLi4ueo9xd3dHRkYGACAjI0OvGNLt1+0rz8KFC7FgwQLjngwRASj5y/CjQf6YtOF0uTELB/nLonWAgEfXJBdCHi0kujV4Hj1b3Ro8cikKLWn5EYtuIerTpw+GDh2Kli1bIjg4GD/++CNycnLw/fffV+lxIyMjkZubK92uX79epccjkpsQP0+sHt0GHo+0Dniq7LBaJj8EcqcrCDI0BXrbMzUFZlmUz5QetwYPULIGj5y6zyxh+RGLbiF6lIuLC5577jlcvnwZvXv3RmFhIXJycvRaiTIzM+Hh4QEA8PDwwPHjx/WeQzcLTRdTFltbW9ja2hr/BIhIYkl/GZJpWeKifKZUmTV4OJbOdCy6hehRd+7cwZUrV+Dp6Ym2bduiZs2aOHDggLT/4sWLSE1NRUBAAAAgICAAZ8+eRVZWlhQTGxsLpVIJtbriCwsSUdWzlL8MybQscVE+U7LENXjIwluIZs6ciX79+sHHxwdpaWmIioqCtbU1Xn75ZahUKowfPx4zZsyAq6srlEolpk6dioCAAHTs2BEAEBQUBLVajTFjxmDx4sXIyMjAnDlzEBERwRYgIiIzkXtBUMfJsN8fQ+PIOCy6ILpx4wZefvll3Lp1C3Xr1kWXLl1w9OhR1K1bFwDw6aefwsrKCoMHD0ZBQQGCg4OxcuVK6fHW1tbYvXs3Jk+ejICAADg6OiIsLAzvvvuuuU6JiEj2LHFRPlPSFhs2NsjQODIOhZDLkP6noNFooFKpkJubC6VSae50iIiqtWKtQJdFB5GRm1/mOCIFSqZe/zq71zPZjfrxvgv44tCVx8ZN6dkIM4ObPTaOyleZ3+9qNYaIiIiqP92ifOX9NS7wrC/MyNXaLRELIiIiIhPiau2WiQURERGZlG7afXl00+6f1XV4uFq7ZWJBREREJiX3afe61dorwtXaTY8FERERmZTcp90DXK3dEln0tHsiInr21HE0cB0eA+OqK67WbllYEBERkUlpDVztxdC46ky3WjuZH7vMiIjIpI5cuWXUOCJjYAsRERGZ1Nm/cowaV50VawW7zCwECyIiIjIp+5rWRo2rrvYmpWP+rnPI0BRI2zyUtpj/UgsOqjYDdpkREZFJtfd1NWpcdbQ3KR2TNpzWK4YAIENTgEkbTmNvUrqZMpMvFkRERGRSozs2MGpcdVOsFXh729kKY97edvaZXZjSUrEgIiIik0q8nmPUuOrm6JVbyLlXVGFMzr0iHOWgcpNiQURERCaVnnPfqHHVTcKffxs1joyDBREREZnU79dvGzWuujG0I4wdZqbFgoiIiEyq2MAFFw2Nq26UdhVf2LWycWQcLIiIiMikrBWGrbNjaFx1o7lf8fihysaRcbAgIiIik/qndy2jxlU3CgMLPUPjyDhYEBERkUl5utgbNa66MfTaZbzGmWmxICIiIpNq7+sKF4eKx8fUcqj5zC7M2LFhbYPOv2NDFkSmxIKIiIgszrM5nLqEtZUCHw3yrzBm4SB/XtPMxFgQERGRSR1PyTZoYcLjKdkmysj0Qvw8sXp0G3go7fS2e6rssHp0G17LzAx4cVciIjKprLx8o8ZVVyF+nuit9uDV7i0ECyIiIjIpN2e7xwdVIq46s7ZScPC0hWCXGRERmZQhg6pdnuFB1WSZWBAREZHFYacRmRoLIiIiMilDBlXffsYHVZPlYUFEREQmlWbgVewNjSMyBhZERERkUr+nGni1ewPjiIyBBREREZlUWu49o8YRGQMLIiIiMqksTaFR44iMgQURERGZlIfKsPWFDI0jMgYWREREZFIdfA1biNDQOCJjYEFEREQmFdapwWPXGVL8L47IVFgQERGRSVlbKWBvY11hjL2NNa/pRSbFa5kREZnRhsOXMOen/yfdf7/PcxjdvYkZM6p6x1Oyca+wuMKYe4XFOJ6Szet8kcnIqoVoxYoVaNCgAezs7NChQwccP37c3CkRkYw1eHuPXjEEAHN++n9o8PYeM2VkGrzaPVki2RREW7ZswYwZMxAVFYXTp0+jVatWCA4ORlZWlrlTIyIZelzR8ywXRbzaPVki2RRES5cuxcSJEzFu3Dio1WqsXr0aDg4O+Oabb8ydGhHJzIbDl4waV92093WFp8qu3IHVCgCeKjte7Z5MShYFUWFhIU6dOoXAwEBpm5WVFQIDA5GQkFAqvqCgABqNRu9GRGQsj3aTPW1cdWNtpUBUPzWA0le1192P6qfmoGoyKVkURH///TeKi4vh7u6ut93d3R0ZGRml4hcuXAiVSiXdvL29TZUqEZEshPh5YtXoNqUWX/RQ2WHV6DYI8fM0U2YkV5xlVobIyEjMmDFDuq/RaFgUEREZWYifJ3qrPXA8JRtZeflwcy7pJmPLEJmDLAqiOnXqwNraGpmZmXrbMzMz4eHhUSre1tYWtra2pkqPiGTm/T7PGdQd9n6f50yQjXlZWyk4tZ4sgiy6zGxsbNC2bVscOHBA2qbVanHgwAEEBASYMTMikiND1xl61tcjIrIksiiIAGDGjBlYs2YN1q1bh/Pnz2Py5Mm4e/cuxo0bZ+7UiEiGrn4U+lT7ici4ZNFlBgDDhw/HzZs3MW/ePGRkZKB169bYu3dvqYHWRESmsDcpHQoAoox9iv/t58BiItNRCCHK+v9ID9FoNFCpVMjNzYVSqTR3OkRUzRVrBbosOoj03LJXYlagZLbVr7N7cYAx0VOozO+3bLrMiIgsxfGU7HKLIaCk1Sg9Nx/HU7JNlxSRzLEgIiIyMV7Li8jysCAiIjIxXsuLyPKwICIiMjFey4vI8rAgIiIyMV7Li8jysCAiIjIDXsuLyLLIZh0iIiJLw2t5EVkOFkRERGbEa3kRWQZ2mREREZHssSAiIiIi2WNBRERERLLHgoiIiIhkjwURERERyR4LIiIiIpI9FkREREQkeyyIiIiISPZYEBEREZHscaVqAwghAAAajcbMmRAREZGhdL/but/xirAgMkBeXh4AwNvb28yZEBERUWXl5eVBpVJVGKMQhpRNMqfVapGWlgZnZ2coFMa96KJGo4G3tzeuX78OpVJp1OeuDuR+/gBfA7mfP8DXgOcv7/MHqu41EEIgLy8PXl5esLKqeJQQW4gMYGVlhXr16lXpMZRKpWz/IwA8f4CvgdzPH+BrwPOX9/kDVfMaPK5lSIeDqomIiEj2WBARERGR7LEgMjNbW1tERUXB1tbW3KmYhdzPH+BrIPfzB/ga8Pzlff6AZbwGHFRNREREsscWIiIiIpI9FkREREQkeyyIiIiISPZYEBEREZHssSCyQAUFBWjdujUUCgUSExPNnY5JvfTSS6hfvz7s7Ozg6emJMWPGIC0tzdxpmcTVq1cxfvx4+Pr6wt7eHo0aNUJUVBQKCwvNnZrJfPDBB+jUqRMcHBzg4uJi7nRMYsWKFWjQoAHs7OzQoUMHHD9+3NwpmUx8fDz69esHLy8vKBQK7Nixw9wpmdTChQvx/PPPw9nZGW5ubhgwYAAuXrxo7rRMatWqVWjZsqW0IGNAQAB++ukns+TCgsgCvfXWW/Dy8jJ3GmbRs2dPfP/997h48SL++9//4sqVKxgyZIi50zKJCxcuQKvV4ssvv8S5c+fw6aefYvXq1XjnnXfMnZrJFBYWYujQoZg8ebK5UzGJLVu2YMaMGYiKisLp06fRqlUrBAcHIysry9ypmcTdu3fRqlUrrFixwtypmMXhw4cRERGBo0ePIjY2FkVFRQgKCsLdu3fNnZrJ1KtXDx999BFOnTqFkydPolevXujfvz/OnTtn+mQEWZQff/xRNGvWTJw7d04AEL///ru5UzKrnTt3CoVCIQoLC82dilksXrxY+Pr6mjsNk1u7dq1QqVTmTqPKtW/fXkREREj3i4uLhZeXl1i4cKEZszIPAGL79u3mTsOssrKyBABx+PBhc6diVrVq1RL/+c9/TH5cthBZkMzMTEycOBHr16+Hg4ODudMxu+zsbGzcuBGdOnVCzZo1zZ2OWeTm5sLV1dXcaVAVKCwsxKlTpxAYGChts7KyQmBgIBISEsyYGZlLbm4uAMj2/3xxcTE2b96Mu3fvIiAgwOTHZ0FkIYQQCA8Px6RJk9CuXTtzp2NWs2fPhqOjI2rXro3U1FTs3LnT3CmZxeXLl7F8+XL861//MncqVAX+/vtvFBcXw93dXW+7u7s7MjIyzJQVmYtWq8W0adPQuXNn+Pn5mTsdkzp79iycnJxga2uLSZMmYfv27VCr1SbPgwVRFXv77behUCgqvF24cAHLly9HXl4eIiMjzZ2y0Rn6GujMmjULv//+O37++WdYW1tj7NixENV4QfXKnj8A/PXXXwgJCcHQoUMxceJEM2VuHE9y/kRyExERgaSkJGzevNncqZhc06ZNkZiYiGPHjmHy5MkICwtDcnKyyfPgpTuq2M2bN3Hr1q0KYxo2bIhhw4YhJiYGCoVC2l5cXAxra2uMGjUK69atq+pUq4yhr4GNjU2p7Tdu3IC3tzeOHDliliZUY6js+aelpaFHjx7o2LEjoqOjYWVVvf9ueZL3Pzo6GtOmTUNOTk4VZ2c+hYWFcHBwwNatWzFgwABpe1hYGHJycmTXMqpQKLB9+3a910IupkyZgp07dyI+Ph6+vr7mTsfsAgMD0ahRI3z55ZcmPW4Nkx5NhurWrYu6des+Nu7zzz/H+++/L91PS0tDcHAwtmzZgg4dOlRlilXO0NegLFqtFkDJUgTVVWXO/6+//kLPnj3Rtm1brF27ttoXQ8DTvf/PMhsbG7Rt2xYHDhyQigCtVosDBw5gypQp5k2OTEIIgalTp2L79u2Ii4tjMfQ/Wq3WLN/5LIgsRP369fXuOzk5AQAaNWqEevXqmSMlkzt27BhOnDiBLl26oFatWrhy5Qrmzp2LRo0aVdvWocr466+/0KNHD/j4+ODjjz/GzZs3pX0eHh5mzMx0UlNTkZ2djdTUVBQXF0vrcDVu3Fj6P/EsmTFjBsLCwtCuXTu0b98ey5Ytw927dzFu3Dhzp2YSd+7cweXLl6X7KSkpSExMhKura6nvxGdRREQENm3ahJ07d8LZ2VkaO6ZSqWBvb2/m7EwjMjISffr0Qf369ZGXl4dNmzYhLi4O+/btM30yJp/XRgZJSUmR3bT7M2fOiJ49ewpXV1dha2srGjRoICZNmiRu3Lhh7tRMYu3atQJAmTe5CAsLK/P8Dx06ZO7Uqszy5ctF/fr1hY2NjWjfvr04evSouVMymUOHDpX5foeFhZk7NZMo7//72rVrzZ2aybzyyivCx8dH2NjYiLp164oXXnhB/Pzzz2bJhWOIiIiISPaq/wAFIiIioqfEgoiIiIhkjwURERERyR4LIiIiIpI9FkREREQkeyyIiIiISPZYEBEREZHssSAiIlmKi4uDQqF47PXSGjRogGXLlpkkJyIyHxZERGTRwsPDoVAooFAoYGNjg8aNG+Pdd9/FgwcPnup5O3XqhPT0dKhUKgAlF5R1cXEpFXfixAm8+uqrT3UsIrJ8vJYZEVm8kJAQrF27FgUFBfjxxx8RERGBmjVrIjIy8omf08bGxqBrxPHCtETywBYiIrJ4tra28PDwgI+PDyZPnozAwEDs2rULt2/fxtixY1GrVi04ODigT58+uHTpkvS4a9euoV+/fqhVqxYcHR3RokUL/PjjjwD0u8zi4uIwbtw45ObmSq1R8+fPB1C6yyw1NRX9+/eHk5MTlEolhg0bhszMTGn//Pnz0bp1a6xfvx4NGjSASqXCiBEjkJeXZ5LXioieDAsiIqp27O3tUVhYiPDwcJw8eRK7du1CQkIChBB48cUXUVRUBKDkauIFBQWIj4/H2bNnsWjRIjg5OZV6vk6dOmHZsmVQKpVIT09Heno6Zs6cWSpOq9Wif//+yM7OxuHDhxEbG4s///wTw4cP14u7cuUKduzYgd27d2P37t04fPgwPvroo6p5MYjIKNhlRkTVhhACBw4cwL59+9CnTx/s2LEDv/32Gzp16gQA2LhxI7y9vbFjxw4MHToUqampGDx4MPz9/QEADRs2LPN5bWxsoFKpoFAoKuxGO3DgAM6ePYuUlBR4e3sDAL799lu0aNECJ06cwPPPPw+gpHCKjo6Gs7MzAGDMmDE4cOAAPvjgA6O9FkRkXGwhIiKLt3v3bjg5OcHOzg59+vTB8OHDER4ejho1aqBDhw5SXO3atdG0aVOcP38eAPD666/j/fffR+fOnREVFYUzZ848VR7nz5+Ht7e3VAwBgFqthouLi3RMoKSbTVcMAYCnpyeysrKe6thEVLVYEBGRxevZsycSExNx6dIl3L9/H+vWrYNCoXjs4yZMmIA///wTY8aMwdmzZ9GuXTssX768yvOtWbOm3n2FQgGtVlvlxyWiJ8eCiIgsnqOjIxo3boz69eujRo2Snv7mzZvjwYMHOHbsmBR369YtXLx4EWq1Wtrm7e2NSZMmYdu2bXjzzTexZs2aMo9hY2OD4uLiCvNo3rw5rl+/juvXr0vbkpOTkZOTo3dMIqp+WBARUbXUpEkT9O/fHxMnTsSvv/6KP/74A6NHj8Y//vEP9O/fHwAwbdo07Nu3DykpKTh9+jQOHTqE5s2bl/l8DRo0wJ07d3DgwAH8/fffuHfvXqmYwMBA+Pv7Y9SoUTh9+jSOHz+OsWPHonv37mjXrl2Vni8RVS0WRERUba1duxZt27ZF3759ERAQACEEfvzxR6nLqri4GBEREWjevDlCQkLw3HPPYeXKlWU+V6dOnTBp0iQMHz4cdevWxeLFi0vFKBQK7Ny5E7Vq1UK3bt0QGBiIhg0bYsuWLVV6nkRU9RRCCGHuJIiIiIjMiS1EREREJHssiIiIiEj2WBARERGR7LEgIiIiItljQURERESyx4KIiIiIZI8FEREREckeCyIiIiKSPRZEREREJHssiIiIiEj2WBARERGR7LEgIiIiItn7/wCYNM8gs66DAAAAAElFTkSuQmCC"
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -414,25 +448,17 @@
     "plt.ylabel('Value')\n",
     "plt.title(f'Agent Position vs Value at fundamental {fundamental_val}')\n",
     "plt.show()\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-03-27T23:40:00.547410Z",
-     "start_time": "2024-03-27T23:40:00.470896Z"
-    }
-   },
-   "id": "31f002d1c196705a",
-   "execution_count": 5
+   ]
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [],
+   "execution_count": null,
+   "id": "d8c2f016f622c77c",
    "metadata": {
     "collapsed": false
    },
-   "id": "d8c2f016f622c77c"
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -444,14 +470,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,