diff --git a/catalyst/examples/dual_moving_average.py b/catalyst/examples/dual_moving_average.py
index ff5dfc5e..363edba1 100644
--- a/catalyst/examples/dual_moving_average.py
+++ b/catalyst/examples/dual_moving_average.py
@@ -20,8 +20,8 @@ def initialize(context):
 
 def handle_data(context, data):
     # define the windows for the moving averages
-    short_window = 50
-    long_window = 200
+    short_window = 2
+    long_window = 2
 
     # Skip as many bars as long_window to properly compute the average
     context.i += 1
@@ -32,16 +32,18 @@ def handle_data(context, data):
     # moving average with the appropriate parameters. We choose to use
     # minute bars for this simulation -> freq="1m"
     # Returns a pandas dataframe.
-    short_mavg = data.history(context.asset,
+    short_data = data.history(context.asset,
                               'price',
                               bar_count=short_window,
-                              frequency="1m",
-                              ).mean()
-    long_mavg = data.history(context.asset,
+                              frequency="1T",
+                              )
+    short_mavg = short_data.mean()
+    long_data = data.history(context.asset,
                              'price',
                              bar_count=long_window,
-                             frequency="1m",
-                             ).mean()
+                             frequency="1T",
+                             )
+    long_mavg = long_data.mean()
 
     # Let's keep the price of our asset in a more handy variable
     price = data.current(context.asset, 'price')
@@ -82,7 +84,6 @@ def handle_data(context, data):
 
 
 def analyze(context, perf):
-
     # Get the base_currency that was passed as a parameter to the simulation
     exchange = list(context.exchanges.values())[0]
     base_currency = exchange.base_currency.upper()
@@ -93,7 +94,7 @@ def analyze(context, perf):
     ax1.legend_.remove()
     ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
     start, end = ax1.get_ylim()
-    ax1.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+    ax1.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
     # Second chart: Plot asset price, moving averages and buys/sells
     ax2 = plt.subplot(412, sharex=ax1)
@@ -104,9 +105,9 @@ def analyze(context, perf):
     ax2.set_ylabel('{asset}\n({base})'.format(
         asset=context.asset.symbol,
         base=base_currency
-        ))
+    ))
     start, end = ax2.get_ylim()
-    ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+    ax2.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
     transaction_df = extract_transactions(perf)
     if not transaction_df.empty:
@@ -136,28 +137,40 @@ def analyze(context, perf):
     ax3.legend_.remove()
     ax3.set_ylabel('Percent Change')
     start, end = ax3.get_ylim()
-    ax3.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+    ax3.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
     # Fourth chart: Plot our cash
     ax4 = plt.subplot(414, sharex=ax1)
     perf.cash.plot(ax=ax4)
     ax4.set_ylabel('Cash\n({})'.format(base_currency))
     start, end = ax4.get_ylim()
-    ax4.yaxis.set_ticks(np.arange(0, end, end/5))
+    ax4.yaxis.set_ticks(np.arange(0, end, end / 5))
 
     plt.show()
 
 
 if __name__ == '__main__':
     run_algorithm(
-            capital_base=1000,
-            data_frequency='minute',
-            initialize=initialize,
-            handle_data=handle_data,
-            analyze=analyze,
-            exchange_name='bitfinex',
-            algo_namespace=NAMESPACE,
-            base_currency='usd',
-            start=pd.to_datetime('2017-9-22', utc=True),
-            end=pd.to_datetime('2017-9-23', utc=True),
-        )
+        capital_base=1000,
+        data_frequency='minute',
+        initialize=initialize,
+        handle_data=handle_data,
+        analyze=analyze,
+        exchange_name='bitfinex',
+        algo_namespace=NAMESPACE,
+        base_currency='usd',
+        simulate_orders=True,
+        live=True,
+    )
+    # run_algorithm(
+    #         capital_base=1000,
+    #         data_frequency='minute',
+    #         initialize=initialize,
+    #         handle_data=handle_data,
+    #         analyze=analyze,
+    #         exchange_name='bitfinex',
+    #         algo_namespace=NAMESPACE,
+    #         base_currency='usd',
+    #         start=pd.to_datetime('2017-9-22', utc=True),
+    #         end=pd.to_datetime('2017-9-23', utc=True),
+    #     )