diff --git a/analysis/index_batch.py b/analysis/index_batch.py
index 336b248..8da42fc 100644
--- a/analysis/index_batch.py
+++ b/analysis/index_batch.py
@@ -1,37 +1,49 @@
 from analysis.market_snapshot import Market, available_date_ranges
+from db_layer.capturas_interface import capturas_interface
+from db_layer.indices_interface import indices_interface
+
 
 class IndexMM:
 
     def __init__(self):
         self.name = 'indexmm'
+        self.market = None
         self.date = None
         self.data = None
+        self.value = None
 
     def calculate(self, market):
         self.market = market
         self.date = self.market.end_date
-        self.data = market.get_market_data()
+        self.data = self.market.get_market_data()
 
-        data_coche_pequeno = {'count': self.data[self.data['tamano_categorico'] == 'coche pequeño'].count(),
-                              'mean': self.data[self.data['tamano_categorico' == 'coche pequeño']]['precio'].transform('mean')}
-        data_coche_grande = {'count': self.data[self.data['tamano_categorico'] == 'coche grande'].count(),
-                              'mean': self.data[self.data['tamano_categorico' == 'coche grande']]['precio'].transform('mean')}
-        data_coche_moto = {'count': self.data[self.data['tamano_categorico'] == 'coche y moto'].count(),
-                              'mean': self.data[self.data['tamano_categorico' == 'coche y moto']]['precio'].transform('mean')}
+        data_coche_pequeno = {'count': self.data[self.data['tamano_categorico'] == 'coche pequeño'].shape[0],
+                              'mean': self.data[self.data['tamano_categorico'] == 'coche pequeño']['precio'].mean()}
+        data_coche_grande = {'count': self.data[self.data['tamano_categorico'] == 'coche grande'].shape[0],
+                              'mean': self.data[self.data['tamano_categorico'] == 'coche grande']['precio'].mean()}
+        data_coche_moto = {'count': self.data[self.data['tamano_categorico'] == 'coche y moto'].shape[0],
+                              'mean': self.data[self.data['tamano_categorico'] == 'coche y moto']['precio'].mean()}
 
         self.value = (((data_coche_grande['count'] * data_coche_grande['mean']) + (data_coche_moto['count'] * data_coche_moto['mean'])
                         + (data_coche_pequeno['count'] * data_coche_pequeno['mean']))
                       / (data_coche_grande['count'] + data_coche_moto['count'] + data_coche_pequeno['count']))
 
-        #SEGUIR AQUI
-
-
-
-
-
     def get_data(self):
         return {'name': self.name,
                 'date': self.date,
-                'value': self.value}
+                'value': self.value.item()}
+
+
+if __name__ == '__main__':
+    for date_range in available_date_ranges:
+        market = Market()
+        market.load_market(capturas_interface.get_market_snapshot(date_range['start'], date_range['end']),
+                           date_range=date_range)
+        market.clean_market('index')
+
+        index = IndexMM()
+        index.calculate(market)
+
+        indices_interface.write_index(index.get_data())
 
 
diff --git a/analysis/market_snapshot.py b/analysis/market_snapshot.py
index b735ca9..149b88b 100644
--- a/analysis/market_snapshot.py
+++ b/analysis/market_snapshot.py
@@ -18,27 +18,29 @@ available_date_ranges = [{'start': datetime(2017, 10, 1), 'end': datetime(2018,
 class Market:
 
     def __init__(self):
-        self.start_date = datetime.today() - timedelta(days=90)
-        self.end_date = datetime.today()
+        self.start_date = None
+        self.end_date = None
         self.market = None
 
-    def load_market(self, market_query_results):
+    def load_market(self, market_query_results, date_range):
         self.market = pd.DataFrame(market_query_results)
+        self.start_date = date_range['start']
+        self.end_date = date_range['end']
 
     def clean_market(self, method):
 
         if method == 'index':
-            self.market.dropna(subset=['tamano_categorico'])
+            self.market = self.market.dropna(subset=['tamano_categorico'])
             self.market = self.market[~self.market['tamano_categorico'].isin(['2 coches o más', 'moto'])]
-            self.market.drop_duplicates(subset=['precio', 'latitud', 'longitud'], keep='last')
+            self.market = self.market.drop_duplicates(subset=['tamano_categorico', 'precio', 'latitud', 'longitud', 'telefono'], keep='last')
             self.market = self.market[self.market['tipo_anuncio'] == 1]
             self.delete_outliers()
 
         if method == 'valoracion':
-            self.market.dropna(subset=['tamano_categorico'])
+            self.market = self.market.dropna(subset=['tamano_categorico'])
             self.market = self.market[~self.market['tamano_categorico'].isin(['2 coches o más', 'moto'])]
             self.market = self.market[self.market['precision'].isin(['ROOFTOP'])]
-            self.market.drop_duplicates(subset=['precio', 'latitud', 'longitud'], keep='last')
+            self.market = self.market.drop_duplicates(subset=['tamano_categorico', 'precio', 'latitud', 'longitud', 'telefono'], keep='last')
             self.market = self.market[self.market['tipo_anuncio'] == 1]
             self.delete_outliers()
 
diff --git a/core/config.py b/core/config.py
index 07d3f6c..232d6a7 100644
--- a/core/config.py
+++ b/core/config.py
@@ -14,7 +14,7 @@ databases = {'dev':{'host': '185.166.215.170 ',
 try:
     current_db = environ['DROGON_ENV']
 except KeyError:
-    current_db = 'pro'
+    current_db = 'dev'
 
 try:
     current_db_parameters = databases[current_db]
diff --git a/db_layer/capturas_interface.py b/db_layer/capturas_interface.py
index 46a810a..db11749 100644
--- a/db_layer/capturas_interface.py
+++ b/db_layer/capturas_interface.py
@@ -111,7 +111,7 @@ class CapturasInterface:
                                     WHERE (`t1`.`referencia` = `t2`.`referencia`)
                                  )
                               )
-                              AND (`t1`.`fecha_captura` BETWEEN %(start_date)S AND %(end_date)S)
+                              AND (`t1`.`fecha_captura` BETWEEN %(start_date)s AND %(end_date)s)
                            )
                                 """
         query_parameters = {'start_date': start_date.strftime('%Y-%m-%d 00:00:00'),
diff --git a/db_layer/db_init_scripts/4_create_valores_indices.sql b/db_layer/db_init_scripts/4_create_valores_indices.sql
new file mode 100644
index 0000000..d83eb50
--- /dev/null
+++ b/db_layer/db_init_scripts/4_create_valores_indices.sql
@@ -0,0 +1,6 @@
+CREATE TABLE `valores_indices` (
+  `fecha_valor` datetime NOT NULL,
+  `nombre_indice` varchar(255) NOT NULL,
+  `valor_indice` double NOT NULL,
+  PRIMARY KEY (`fecha_valor`,`nombre_indice`)
+) ENGINE=InnoDB DEFAULT CHARSET=latin1 ;
diff --git a/db_layer/indices_interface.py b/db_layer/indices_interface.py
new file mode 100644
index 0000000..c4b1069
--- /dev/null
+++ b/db_layer/indices_interface.py
@@ -0,0 +1,21 @@
+from core.mysql_wrapper import get_anunciosdb
+
+
+class IndicesInterface:
+
+    def __init__(self):
+        self.anunciosdb = get_anunciosdb()
+
+    def write_index(self, index_data):
+        query_statement = """ REPLACE INTO valores_indices
+                              (fecha_valor, nombre_indice, valor_indice)
+                              VALUES
+                              (%(date)s, %(name)s, %(value)s)
+                              """
+
+        query_parameters = index_data
+
+        self.anunciosdb.query(query_statement, query_parameters)
+
+
+indices_interface = IndicesInterface()
diff --git a/tests/index_batch_tests.py b/tests/index_batch_tests.py
new file mode 100644
index 0000000..f7cbbf2
--- /dev/null
+++ b/tests/index_batch_tests.py
@@ -0,0 +1,106 @@
+from analysis.market_snapshot import Market
+from analysis.index_batch import IndexMM
+import pandas as pd
+
+sample_market = [
+                 {'tamano_categorico': 'coche pequeño',
+                  'tipo_anuncio': 1,
+                  'precio': 15000,
+                  'calle': 'B1',
+                  'telefono': 123,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': 'coche pequeño',
+                  'tipo_anuncio': 1,
+                  'precio': 20000,
+                  'calle': 'B2',
+                  'telefono': 321,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': 'coche grande',
+                  'tipo_anuncio': 1,
+                  'precio': 20000,
+                  'calle': 'B2',
+                  'telefono': 321,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': 'coche grande',
+                  'tipo_anuncio': 1,
+                  'precio': 25000,
+                  'calle': 'B2',
+                  'telefono': 123,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': 'coche y moto',
+                  'tipo_anuncio': 1,
+                  'precio': 22000,
+                  'calle': 'B1',
+                  'telefono': 456,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': 'coche y moto',
+                  'tipo_anuncio': 1,
+                  'precio': 26000,
+                  'calle': 'B3',
+                  'telefono': 789,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': None,
+                  'tipo_anuncio': 1,
+                  'precio': 15000,
+                  'calle': 'abc',
+                  'telefono': 456,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': 'moto',
+                  'tipo_anuncio': 1,
+                  'precio': 3000,
+                  'calle': 'B4',
+                  'telefono': 123,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': '2 coches o más',
+                  'tipo_anuncio': 1,
+                  'precio': 60000,
+                  'calle': 'B4',
+                  'telefono': 123,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': 'coche pequeño',
+                  'tipo_anuncio': 1,
+                  'precio': 20000,
+                  'calle': 'B2',
+                  'telefono': 321,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': 'coche pequeño',
+                  'tipo_anuncio': 2,
+                  'precio': 50,
+                  'calle': 'B4',
+                  'telefono': 123,
+                  'latitud': 2.1,
+                  'longitud': 1.2},
+                 {'tamano_categorico': 'moto',
+                  'tipo_anuncio': 1,
+                  'precio': 300000,
+                  'calle': 'B4',
+                  'telefono': 123,
+                  'latitud': 2.1,
+                  'longitud': 1.2}
+                 ]
+date_range = {'start': '2018-01-01 00:00:00',
+              'end': '2018-02-01 00:00:00'}
+
+
+market = Market()
+market.load_market(sample_market,
+                   date_range=date_range)
+market.market.fillna(value=pd.np.nan, inplace=True)
+print(market.market.to_string())
+market.clean_market('index')
+print(market.market.to_string())
+
+index = IndexMM()
+index.calculate(market)
+index.get_data()
+