++

main.py

@@ -6,57 +6,47 @@ import httpx
 from httpx import BasicAuth
 
 
-class BrainLogin:
+class DataSetDownloader:
-    def __init__(self, credentials_file='account.txt'):
+    def __init__(self):
-        self.credentials_file = credentials_file
+        self.base_api_url = 'https://api.worldquantbrain.com'
-        self.client = None
+        self.client = self.login()
-        self.brain_api_url = 'https://api.worldquantbrain.com'
-
-    def load_credentials(self):
-        if not os.path.exists(self.credentials_file):
-            print("未找到 account.txt 文件")
-            with open(self.credentials_file, 'w') as f:
-                f.write("")
-            print("account.txt 文件已创建，请填写账号密码, 格式: ['username', 'password']")
-            exit(1)
-
-        with open(self.credentials_file) as f:
-            credentials = eval(f.read())
-        return credentials[0], credentials[1]
 
     def login(self):
-        try:
+        """登录并返回客户端实例"""
-            username, password = self.load_credentials()
+        username, password = "jack0210_@hotmail.com", "!QAZ2wsx+0913"
-            self.client = httpx.Client(auth=BasicAuth(username, password))
+        client = httpx.Client(auth=BasicAuth(username, password))
 
-            response = self.client.post(f'{self.brain_api_url}/authentication')
+        try:
+            response = client.post(f'{self.base_api_url}/authentication')
             print(f"登录状态: {response.status_code}")
 
             if response.status_code in [200, 201]:
                 print("登录成功!")
-                print(f"账户信息: {response.json()}")
+                return client
-                return self.client
             else:
                 print(f"登录失败: {response.json()}")
                 return None
-
         except Exception as e:
             print(f"登录过程中出现错误: {e}")
             return None
 
-    def get_client(self):
+    def _debug_response(self, endpoint, data_set_id, offset=0, limit=20):
-        return self.client
+        """调试请求响应"""
+        print(f"\n=== 调试请求: {endpoint} ===")
+        url = f"{self.base_api_url}/{data_set_id}"
+        params = self._build_params(data_set_id, offset, limit)
 
+        response = self.client.get(url, params=params)
 
-class DataSetDownloader:
+        if response.status_code == 200:
-    def __init__(self, client):
+            data = response.json()
-        self.client = client
+            print(f"count: {data.get('count')}")
-        self.base_api_url = 'https://api.worldquantbrain.com'
+            print(f"results 长度: {len(data.get('results', []))}")
+            print(f"响应键: {list(data.keys())}")
 
-    def debug_detailed_response(self, endpoint, data_set_id, offset, limit=20):
+    def _build_params(self, data_set_id, offset=0, limit=50):
-        print(f"\n=== 调试请求: {endpoint} ===")
+        """构建请求参数"""
-        url = f"{self.base_api_url}/{endpoint}"
+        return {
-        params = {
             'dataset.id': data_set_id,
             'delay': 1,
             'instrumentType': 'EQUITY',
@@ -66,51 +56,38 @@ class DataSetDownloader:
             'universe': 'TOP3000'
         }
 
-        response = self.client.get(url, params=params)
+    def _process_item(self, item):
-
+        """处理单个数据项"""
-        if response.status_code == 200:
+        return {
-            data = response.json()
+            'id': item.get('id', ''),
-            print(f"count: {data.get('count')}")
+            'description': item.get('description', ''),
-            print(f"results 长度: {len(data.get('results', []))}")
+            'dataset_id': item.get('dataset', {}).get('id', ''),
-            print(f"响应键: {list(data.keys())}")
+            'dataset_name': item.get('dataset', {}).get('name', ''),
+            'category_id': item.get('category', {}).get('id', ''),
+            'category_name': item.get('category', {}).get('name', ''),
+            'region': item.get('region', ''),
+            'delay': item.get('delay', ''),
+            'universe': item.get('universe', ''),
+            'type': item.get('type', '')
+        }
 
-    def process_data(self, raw_data):
+    def _process_data(self, raw_data):
-        processed_data = []
+        """批量处理数据"""
-
+        return [self._process_item(item) for item in raw_data]
-        for item in raw_data:
-            processed_item = {
-                'id': item.get('id', ''),
-                'description': item.get('description', ''),
-                'dataset_id': item.get('dataset', {}).get('id', ''),
-                'dataset_name': item.get('dataset', {}).get('name', ''),
-                'category_id': item.get('category', {}).get('id', ''),
-                'category_name': item.get('category', {}).get('name', ''),
-                'region': item.get('region', ''),
-                'delay': item.get('delay', ''),
-                'universe': item.get('universe', ''),
-                'type': item.get('type', '')
-            }
-            processed_data.append(processed_item)
-
-        return processed_data
 
     def download_data_set(self, endpoint, data_set_id):
-        output_dir = 'reference_fields'
+        """下载数据集"""
-        if not os.path.exists(output_dir):
+        # 检查登录状态
-            os.makedirs(output_dir)
+        if not self.client:
+            print("❌ 客户端未初始化，无法下载数据")
+            return
 
-        self.debug_detailed_response(endpoint, data_set_id, offset=0, limit=20)
+        # 调试请求
+        self._debug_response(endpoint, data_set_id, offset=0, limit=20)
 
+        # 获取数据总数
         url = f"{self.base_api_url}/{endpoint}"
-        params = {
+        params = self._build_params(data_set_id, limit=1)
-            'dataset.id': data_set_id,
-            'delay': 1,
-            'instrumentType': 'EQUITY',
-            'limit': 1,
-            'offset': 0,
-            'region': 'USA',
-            'universe': 'TOP3000'
-        }
 
         response = self.client.get(url, params=params)
         data = response.json()
@@ -122,24 +99,15 @@ class DataSetDownloader:
             print("❌ 没有找到数据")
             return
 
+        # 下载所有数据
         limit = 50
         all_data = []
-
         print("🚀 开始下载数据...")
+
         for offset in range(0, total_count, limit):
-            sleep_time = random.uniform(1.0, 1.5)
+            time.sleep(random.uniform(1.0, 1.5))
-            time.sleep(sleep_time)
-
-            params = {
-                'dataset.id': data_set_id,
-                'delay': 1,
-                'instrumentType': 'EQUITY',
-                'limit': limit,
-                'offset': offset,
-                'region': 'USA',
-                'universe': 'TOP3000'
-            }
 
+            params = self._build_params(data_set_id, offset, limit)
             print(f"📥 下载进度: {offset}/{total_count} ({offset / total_count * 100:.1f}%)")
 
             try:
@@ -151,7 +119,6 @@ class DataSetDownloader:
 
                     print(f"✅ 本页获取到 {len(results)} 条记录")
                     all_data.extend(results)
-                    print(f"✅ 累计获取 {len(all_data)} 条记录")
 
                     if len(results) < limit:
                         print("🎯 到达数据末尾")
@@ -160,13 +127,21 @@ class DataSetDownloader:
                     print(f"❌ 请求失败: {response.status_code}")
                     break
 
+                time.sleep(random.uniform(1, 2))
+
             except Exception as e:
                 print(f"❌ 下载过程中出错: {e}")
                 break
 
+        # 处理并保存数据
         print("🔄 处理数据中...")
-        processed_data = self.process_data(all_data)
+        processed_data = self._process_data(all_data)
 
+        # 确保输出目录存在
+        output_dir = 'reference_fields'
+        os.makedirs(output_dir, exist_ok=True)
+
+        # 保存数据
         output_file = os.path.join(output_dir, f"{data_set_id}_{endpoint}.json")
         with open(output_file, 'w', encoding='utf-8') as f:
             json.dump(processed_data, f, ensure_ascii=False, indent=2)
@@ -174,22 +149,14 @@ class DataSetDownloader:
         print(f"💾 处理后的数据已保存到: {output_file}")
         print(f"🎉 总共处理了 {len(processed_data)} 条记录")
 
-        if processed_data:
-            print("\n📋 处理后数据示例:")
-            print(json.dumps(processed_data[0], indent=2, ensure_ascii=False))
-
 
 if __name__ == "__main__":
-    brain_login = BrainLogin()
+    downloader = DataSetDownloader()
-    client = brain_login.login()
-
-    if client:
-        downloader = DataSetDownloader(client)
-
-        endpoint_list = ['data-sets', 'data-fields']
-        endpoint = endpoint_list[0]
-        data_set_id = 'analyst4'
 
+    if downloader.client:
+        # endpoint = 'data-sets'
+        endpoint = 'data-fields'
+        data_set_id = 'socialmedia8'
         downloader.download_data_set(endpoint, data_set_id)
     else:
-        print("❌ 登录失败，无法下载数据")
+        print("❌ 登录失败，无法下载数据")

reference_fields/model16_data-fields.json

@@ -0,0 +1,290 @@
+[
+  {
+    "id": "analyst_revision_rank_derivative",
+    "description": "Change in ranking for analyst revisions and momentum compared to previous period.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "cashflow_efficiency_rank_derivative",
+    "description": "Change in ranking for cash flow generation and profitability compared to previous period.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "composite_factor_score_derivative",
+    "description": "Change in overall composite factor score from the prior period.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "earnings_certainty_rank_derivative",
+    "description": "Change in ranking for earnings sustainability and certainty compared to previous period.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_bfl_growth",
+    "description": "The purpose of this metric is to qualify the expected MT growth potential of the stock.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_bfl_momentum",
+    "description": "The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_bfl_profitability",
+    "description": "The purpose of this metric is to rank stock based on their ability to generate cash flows.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_bfl_quality",
+    "description": "The purpose of this metric is to measure both the sustainability and certainty of earnings.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_bfl_surface",
+    "description": "The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_bfl_surface_accel",
+    "description": "The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_bfl_total",
+    "description": "The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_bfl_value",
+    "description": "The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_growth",
+    "description": "The purpose of this metric is to qualify the expected MT growth potential of the stock.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_momentum",
+    "description": "The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_profitability",
+    "description": "The purpose of this metric is to rank stock based on their ability to generate cash flows.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_quality",
+    "description": "The purpose of this metric is to measure both the sustainability and certainty of earnings.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_surface",
+    "description": "The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_surface_accel",
+    "description": "The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_total",
+    "description": "The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "fscore_value",
+    "description": "The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "growth_potential_rank_derivative",
+    "description": "Change in ranking for medium-term growth potential compared to previous period.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "multi_factor_acceleration_score_derivative",
+    "description": "Change in the acceleration of multi-factor score compared to previous period.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "multi_factor_static_score_derivative",
+    "description": "Change in static multi-factor score compared to previous period.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "relative_valuation_rank_derivative",
+    "description": "Change in ranking for valuation metrics compared to previous period.",
+    "dataset_id": "model16",
+    "dataset_name": "Fundamental Scores",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  }
+]

reference_fields/model51_data-fields.json

@@ -0,0 +1,194 @@
+[
+  {
+    "id": "beta_last_30_days_spy",
+    "description": "Beta to SPY in 30 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "beta_last_360_days_spy",
+    "description": "Beta to SPY in 360 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "beta_last_60_days_spy",
+    "description": "Beta to SPY in 60 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "beta_last_90_days_spy",
+    "description": "Beta to SPY in 90 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "correlation_last_30_days_spy",
+    "description": "Correlation to SPY in 30 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "correlation_last_360_days_spy",
+    "description": "Correlation to SPY in 360 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "correlation_last_60_days_spy",
+    "description": "Correlation to SPY in 60 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "correlation_last_90_days_spy",
+    "description": "Correlation to SPY in 90 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "systematic_risk_last_30_days",
+    "description": "Systematic Risk Last 30 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "systematic_risk_last_360_days",
+    "description": "Systematic Risk Last 360 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "systematic_risk_last_60_days",
+    "description": "Systematic Risk Last 60 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "systematic_risk_last_90_days",
+    "description": "Systematic Risk Last 90 Days",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "unsystematic_risk_last_30_days",
+    "description": "Unsystematic Risk Last 30 Days - Relative to SPY",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "unsystematic_risk_last_360_days",
+    "description": "Unsystematic Risk Last 360 Days - Relative to SPY",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "unsystematic_risk_last_60_days",
+    "description": "Unsystematic Risk Last 60 Days - Relative to SPY",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "unsystematic_risk_last_90_days",
+    "description": "Unsystematic Risk Last 90 Days - Relative to SPY",
+    "dataset_id": "model51",
+    "dataset_name": "Systematic Risk Metrics",
+    "category_id": "model",
+    "category_name": "Model",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  }
+]

reference_fields/news18_data-fields.json

@@ -0,0 +1,902 @@
+[
+  {
+    "id": "nws18_acb",
+    "description": "News sentiment specializing in corporate action announcements",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_bam",
+    "description": "News sentiment specializing in mergers and acquisitions",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_bee",
+    "description": "News sentiment specializing in growth of earnings",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_ber",
+    "description": "News sentiment specializing in earnings result",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_event_relevance",
+    "description": "Relevance of the event to the story",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_event_similarity_days",
+    "description": "Days since a similar event was detected",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_ghc_lna",
+    "description": "Change in analyst recommendation",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_nip",
+    "description": "Degree of impact of the news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_qcm",
+    "description": "News sentiment of relevant news with high confidence",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_qep",
+    "description": "News sentiment based on positive and negative words on global equity",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_qmb",
+    "description": "News sentiment specializing in editorials on global markets",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_relevance",
+    "description": "Relevance of news to the company",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_ssc",
+    "description": "Sentiment of the news calculated using multiple techniques",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "nws18_sse",
+    "description": "Sentiment of phrases impacting the company",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "rp_css_assets",
+    "description": "Composite sentiment score of assets news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_business",
+    "description": "Composite sentiment score of business-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_credit",
+    "description": "Composite sentiment score of credit news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_credit_ratings",
+    "description": "Composite sentiment score of credit ratings news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_dividends",
+    "description": "Composite sentiment score of dividends news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_earnings",
+    "description": "Composite sentiment score of earnings news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_equity",
+    "description": "Composite sentiment score of equity action news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_insider",
+    "description": "Composite sentiment score of insider trading news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_inverstor",
+    "description": "Composite sentiment score of investor relations news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_labor",
+    "description": "Composite sentiment score of labor issues news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_legal",
+    "description": "Composite sentiment score of legal news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_marketing",
+    "description": "Composite sentiment score of marketing news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_mna",
+    "description": "Composite sentiment score of mergers and acquisitions-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_partner",
+    "description": "Composite sentiment score of partnership news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_price",
+    "description": "Composite sentiment score of stock price news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_product",
+    "description": "Composite sentiment score of product and service-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_ptg",
+    "description": "Composite sentiment score of price target news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_ratings",
+    "description": "Composite sentiment score of analyst ratings-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_revenue",
+    "description": "Composite sentiment score of revenue news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_society",
+    "description": "Composite sentiment score of society-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_css_technical",
+    "description": "Composite sentiment score based on technical analysis",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_assets",
+    "description": "Event sentiment score of assets news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_business",
+    "description": "Event sentiment score of business-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_credit",
+    "description": "Event sentiment score of credit news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_credit_ratings",
+    "description": "Event sentiment score of credit ratings news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_dividends",
+    "description": "Event sentiment score of dividends news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_earnings",
+    "description": "Event sentiment score of earnings news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_equity",
+    "description": "Event sentiment score of equity action news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_insider",
+    "description": "Event sentiment score of insider trading news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_labor",
+    "description": "Event sentiment score of labor issues news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_legal",
+    "description": "Event sentiment score of legal news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_mna",
+    "description": "Event sentiment score of mergers and acquisitions-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_partner",
+    "description": "Event sentiment score of partnership news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_price",
+    "description": "Event sentiment score of stock price news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_product",
+    "description": "Event sentiment score of product and service-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_ptg",
+    "description": "Event sentiment score of price target news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_ratings",
+    "description": "Event sentiment score of analyst ratings-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_revenue",
+    "description": "Event sentiment score of revenue news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_society",
+    "description": "Event sentiment score of society-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_ess_technical",
+    "description": "Event sentiment score based on technical analysis",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_assets",
+    "description": "News impact projection of assets news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_business",
+    "description": "News impact projection of business-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_credit",
+    "description": "News impact projection of credit news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_credit_ratings",
+    "description": "News impact projection of credit ratings news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_dividends",
+    "description": "News impact projection of dividends news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_earnings",
+    "description": "News impact projection of earnings news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_equity",
+    "description": "News impact projection of equity action news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_insider",
+    "description": "News impact projection of insider trading news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_inverstor",
+    "description": "News impact projection of investor relations news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_labor",
+    "description": "News impact projection of labor issues news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_legal",
+    "description": "News impact projection of legal news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_marketing",
+    "description": "News impact projection of marketing news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_mna",
+    "description": "News impact projection of mergers and acquisitions-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_partner",
+    "description": "News impact projection of partnership news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_price",
+    "description": "News impact projection of stock price news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_product",
+    "description": "News impact projection of product and service-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_ptg",
+    "description": "News impact projection of price target news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_ratings",
+    "description": "News impact projection of analyst ratings-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_revenue",
+    "description": "News impact projection of revenue news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_society",
+    "description": "News impact projection of society-related news",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "rp_nip_technical",
+    "description": "News impact projection based on technical analysis",
+    "dataset_id": "news18",
+    "dataset_name": "Ravenpack News Data",
+    "category_id": "news",
+    "category_name": "News",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  }
+]

reference_fields/option8_data-fields.json

@@ -0,0 +1,770 @@
+[
+  {
+    "id": "historical_volatility_10",
+    "description": "Close-to-close Historical volatility over 10 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "historical_volatility_120",
+    "description": "Close-to-close Historical volatility over 120 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "historical_volatility_150",
+    "description": "Close-to-close Historical volatility over 150 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "historical_volatility_180",
+    "description": "Close-to-close Historical volatility over 180 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "historical_volatility_20",
+    "description": "Close-to-close Historical volatility over 20 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "historical_volatility_30",
+    "description": "Close-to-close Historical volatility over 30 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "historical_volatility_60",
+    "description": "Close-to-close Historical volatility over 60 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "historical_volatility_90",
+    "description": "Close-to-close Historical volatility over 90 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_10",
+    "description": "At-the-money option-implied volatility for call Option for 10 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_1080",
+    "description": "At-the-money option-implied volatility for call option for 1080 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_120",
+    "description": "At-the-money option-implied volatility for call Option for 120 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_150",
+    "description": "At-the-money option-implied volatility for call Option for 150 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_180",
+    "description": "At-the-money option-implied volatility for call Option for 180 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_20",
+    "description": "At-the-money option-implied volatility for call Option for 20 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_270",
+    "description": "At-the-money option-implied volatility for call Option for 270 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_30",
+    "description": "At-the-money option-implied volatility for call Option for 30 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_360",
+    "description": "At-the-money option-implied volatility for call Option for 360 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_60",
+    "description": "At-the-money option-implied volatility for call Option for 60 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_720",
+    "description": "At-the-money option-implied volatility for call Option for 720 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_call_90",
+    "description": "At-the-money option-implied volatility for call Option for 90 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_10",
+    "description": "At-the-money option-implied volatility mean for 10 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_1080",
+    "description": "At-the-money option-implied volatility mean for 3 years",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_120",
+    "description": "At-the-money option-implied volatility mean for 120 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_150",
+    "description": "At-the-money option-implied volatility mean for 150 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_180",
+    "description": "At-the-money option-implied volatility mean for 180 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_20",
+    "description": "At-the-money option-implied volatility mean for 20 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_270",
+    "description": "At-the-money option-implied volatility mean for 270 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_30",
+    "description": "At-the-money option-implied volatility mean for 30 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_360",
+    "description": "At-the-money option-implied volatility mean for 360 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_60",
+    "description": "At-the-money option-implied volatility mean for 60 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_720",
+    "description": "At-the-money option-implied volatility mean for 720 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_90",
+    "description": "At-the-money option-implied volatility mean for 90 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_10",
+    "description": "At-the-money option-implied volatility mean skew for 10 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_1080",
+    "description": "At-the-money option-implied volatility mean skew for 3 years",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_120",
+    "description": "At-the-money option-implied volatility mean skew for 120 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_150",
+    "description": "At-the-money option-implied volatility mean skew for 150 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_180",
+    "description": "At-the-money option-implied volatility mean skew for 180 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_20",
+    "description": "At-the-money option-implied volatility mean skew for 20 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_270",
+    "description": "At-the-money option-implied volatility mean skew for 270 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_30",
+    "description": "At-the-money option-implied volatility mean skew for 30 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_360",
+    "description": "At-the-money option-implied volatility mean skew for 360 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_60",
+    "description": "At-the-money option-implied volatility mean skew for 60 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_720",
+    "description": "At-the-money option-implied volatility mean skew for 720 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_mean_skew_90",
+    "description": "At-the-money option-implied volatility mean skew for 90 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_10",
+    "description": "At-the-money option-implied volatility for Put Option for 10 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_1080",
+    "description": "At-the-money option-implied volatility for Put Option for 3 years",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_120",
+    "description": "At-the-money option-implied volatility for Put Option for 120 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_150",
+    "description": "At-the-money option-implied volatility for Put Option for 150 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_180",
+    "description": "At-the-money option-implied volatility for put option for 180 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_20",
+    "description": "At-the-money option-implied volatility for Put Option for 20 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_270",
+    "description": "At-the-money option-implied volatility for Put Option for 270 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_30",
+    "description": "At-the-money option-implied volatility for Put Option for 30 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_360",
+    "description": "At-the-money option-implied volatility for Put Option for 360 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_60",
+    "description": "At-the-money option-implied volatility for Put Option for 60 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_720",
+    "description": "At-the-money option-implied volatility for Put Option for 720 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "implied_volatility_put_90",
+    "description": "At-the-money option-implied volatility for Put Option for 90 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "parkinson_volatility_10",
+    "description": "Parkinson model's historical volatility over 2 weeks",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "parkinson_volatility_120",
+    "description": "Parkinson model's historical volatility over 120 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "parkinson_volatility_150",
+    "description": "Parkinson model's historical volatility over 150 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "parkinson_volatility_180",
+    "description": "Parkinson model's historical volatility over 180 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "parkinson_volatility_20",
+    "description": "Parkinson model's historical volatility over 20 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "parkinson_volatility_30",
+    "description": "Parkinson model's historical volatility over 30 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "parkinson_volatility_60",
+    "description": "Parkinson model's historical volatility over 60 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "parkinson_volatility_90",
+    "description": "Parkinson model's historical volatility over 90 days",
+    "dataset_id": "option8",
+    "dataset_name": "Volatility Data",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  }
+]

reference_fields/option9_data-fields.json

@@ -0,0 +1,890 @@
+[
+  {
+    "id": "call_breakeven_10",
+    "description": "Price at which a stock's call options with expiration 10 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_1080",
+    "description": "Price at which a stock's call options with expiration 1080 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_120",
+    "description": "Price at which a stock's call options with expiration 120 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_150",
+    "description": "Price at which a stock's call options with expiration 150 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_180",
+    "description": "Price at which a stock's call options with expiration 180 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_20",
+    "description": "Price at which a stock's call options with expiration 20 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_270",
+    "description": "Price at which a stock's call options with expiration 270 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_30",
+    "description": "Price at which a stock's call options with expiration 30 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_360",
+    "description": "Price at which a stock's call options with expiration 360 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_60",
+    "description": "Price at which a stock's call options with expiration 60 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_720",
+    "description": "Price at which a stock's call options with expiration 720 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "call_breakeven_90",
+    "description": "Price at which a stock's call options with expiration 90 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_10",
+    "description": "Forward price at 10 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_1080",
+    "description": "Forward price at 1080 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_120",
+    "description": "Forward price at 120 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_150",
+    "description": "Forward price at 150 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_180",
+    "description": "Forward price at 180 days derived from a synthetic long option with payoff similar to long stock + option dynamics. combination of long ATM call, and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_20",
+    "description": "Forward price at 20 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_270",
+    "description": "Forward price at 270 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_30",
+    "description": "Forward price at 30 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_360",
+    "description": "Forward price at 360 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_60",
+    "description": "Forward price at 60 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_720",
+    "description": "Forward price at 720 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "forward_price_90",
+    "description": "Forward price at 90 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_10",
+    "description": "Price at which a stock's options with expiration 10 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_1080",
+    "description": "Price at which a stock's options with expiration 1080 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_120",
+    "description": "Price at which a stock's options with expiration 120 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_150",
+    "description": "Price at which a stock's options with expiration 150 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_180",
+    "description": "Price at which a stock's options with expiration 180 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_20",
+    "description": "Price at which a stock's options with expiration 20 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_270",
+    "description": "Price at which a stock's options with expiration 270 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_30",
+    "description": "Price at which a stock's options with expiration 30 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_360",
+    "description": "Price at which a stock's options with expiration 360 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_60",
+    "description": "Price at which a stock's options with expiration 60 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_720",
+    "description": "Price at which a stock's options with expiration 720 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "option_breakeven_90",
+    "description": "Price at which a stock's options with expiration 90 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_10",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 10 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_1080",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 1080 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_120",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 120 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_150",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 150 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_180",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 180 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_20",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 20 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_270",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 270 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_30",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 30 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_360",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 360 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_60",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 60 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_720",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 720 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_90",
+    "description": "Ratio of put open interest to call open interest on a stock's options with expiration 90 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_oi_all",
+    "description": "Ratio of put open interest to call open interest for all maturities on stock's options.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_10",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 10 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_1080",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 1080 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_120",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 120 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_150",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 150 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_180",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 180 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_20",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 20 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_270",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 270 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_30",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 30 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_360",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 360 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_60",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 60 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_720",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 720 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_90",
+    "description": "Ratio of put volume to call volume on a stock's options with expiration 90 days in the future.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "pcr_vol_all",
+    "description": "Ratio of put volume to call volume for all maturities on stock's options.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_10",
+    "description": "Price at which a stock's put options with expiration 10 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_1080",
+    "description": "Price at which a stock's put options with expiration 1080 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_120",
+    "description": "Price at which a stock's put options with expiration 120 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_150",
+    "description": "Price at which a stock's put options with expiration 150 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_180",
+    "description": "Price at which a stock's put options with expiration 180 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_20",
+    "description": "Price at which a stock's put options with expiration 20 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_270",
+    "description": "Price at which a stock's put options with expiration 270 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_30",
+    "description": "Price at which a stock's put options with expiration 30 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_360",
+    "description": "Price at which a stock's put options with expiration 360 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_60",
+    "description": "Price at which a stock's put options with expiration 60 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_720",
+    "description": "Price at which a stock's put options with expiration 720 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "put_breakeven_90",
+    "description": "Price at which a stock's put options with expiration 90 days in the future break even based on its recent bid/ask mean.",
+    "dataset_id": "option9",
+    "dataset_name": "Options Analytics",
+    "category_id": "option",
+    "category_name": "Option",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  }
+]

reference_fields/pv1_data-fields.json

@@ -0,0 +1,278 @@
+[
+  {
+    "id": "adv20",
+    "description": "Average daily volume in past 20 days",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "cap",
+    "description": "Daily market capitalization (in millions)",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "close",
+    "description": "Daily close price",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "country",
+    "description": "Country grouping",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "GROUP"
+  },
+  {
+    "id": "currency",
+    "description": "Currency",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "GROUP"
+  },
+  {
+    "id": "cusip",
+    "description": "CUSIP Value",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "SYMBOL"
+  },
+  {
+    "id": "dividend",
+    "description": "Dividend",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "exchange",
+    "description": "Exchange grouping",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "GROUP"
+  },
+  {
+    "id": "high",
+    "description": "Daily high price",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "industry",
+    "description": "Industry grouping",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "GROUP"
+  },
+  {
+    "id": "isin",
+    "description": "ISIN Value",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "SYMBOL"
+  },
+  {
+    "id": "low",
+    "description": "Daily low price",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "market",
+    "description": "Market grouping",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "GROUP"
+  },
+  {
+    "id": "open",
+    "description": "Daily open price",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "returns",
+    "description": "Daily returns",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "sector",
+    "description": "Sector grouping",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "GROUP"
+  },
+  {
+    "id": "sedol",
+    "description": "Sedol",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "SYMBOL"
+  },
+  {
+    "id": "sharesout",
+    "description": "Daily outstanding shares (in millions)",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "split",
+    "description": "Stock split ratio",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "subindustry",
+    "description": "Subindustry grouping",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "GROUP"
+  },
+  {
+    "id": "ticker",
+    "description": "Ticker",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "SYMBOL"
+  },
+  {
+    "id": "volume",
+    "description": "Daily volume",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "vwap",
+    "description": "Daily volume weighted average price",
+    "dataset_id": "pv1",
+    "dataset_name": "Price Volume Data for Equity",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  }
+]

reference_fields/socialmedia12_data-fields.json

@@ -0,0 +1,218 @@
+[
+  {
+    "id": "scl12_alltype_buzzvec",
+    "description": "sentiment volume",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "scl12_alltype_sentvec",
+    "description": "sentiment",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "scl12_alltype_typevec",
+    "description": "instrument type index",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "scl12_buzz",
+    "description": "relative sentiment volume",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "scl12_buzz_fast_d1",
+    "description": "relative sentiment volume",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "scl12_buzzvec",
+    "description": "sentiment volume",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "scl12_sentiment",
+    "description": "sentiment",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "scl12_sentiment_fast_d1",
+    "description": "sentiment",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "scl12_sentvec",
+    "description": "sentiment",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "scl12_typevec",
+    "description": "instrument type index",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "VECTOR"
+  },
+  {
+    "id": "snt_buzz",
+    "description": "negative relative sentiment volume, fill nan with 0",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "snt_buzz_bfl",
+    "description": "negative relative sentiment volume, fill nan with 1",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "snt_buzz_bfl_fast_d1",
+    "description": "negative relative sentiment volume, fill nan with 1",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "snt_buzz_fast_d1",
+    "description": "negative relative sentiment volume, fill nan with 0",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "snt_buzz_ret",
+    "description": "negative return of relative sentiment volume",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "snt_buzz_ret_fast_d1",
+    "description": "negative return of relative sentiment volume",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "snt_value",
+    "description": "negative sentiment, fill nan with 0",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "snt_value_fast_d1",
+    "description": "negative sentiment, fill nan with 0",
+    "dataset_id": "socialmedia12",
+    "dataset_name": "Sentiment Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  }
+]

reference_fields/socialmedia8_data-fields.json

@@ -0,0 +1,26 @@
+[
+  {
+    "id": "snt_social_value",
+    "description": "Z score of sentiment",
+    "dataset_id": "socialmedia8",
+    "dataset_name": "Social Media Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  },
+  {
+    "id": "snt_social_volume",
+    "description": "Normalized tweet volume",
+    "dataset_id": "socialmedia8",
+    "dataset_name": "Social Media Data for Equity",
+    "category_id": "socialmedia",
+    "category_name": "Social Media",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "MATRIX"
+  }
+]

reference_fields/univ1_data-fields.json

@@ -0,0 +1,62 @@
+[
+  {
+    "id": "top1000",
+    "description": "20140630",
+    "dataset_id": "univ1",
+    "dataset_name": "Universe Dataset",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "UNIVERSE"
+  },
+  {
+    "id": "top200",
+    "description": "20140630",
+    "dataset_id": "univ1",
+    "dataset_name": "Universe Dataset",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "UNIVERSE"
+  },
+  {
+    "id": "top3000",
+    "description": "20140630",
+    "dataset_id": "univ1",
+    "dataset_name": "Universe Dataset",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "UNIVERSE"
+  },
+  {
+    "id": "top500",
+    "description": "20140630",
+    "dataset_id": "univ1",
+    "dataset_name": "Universe Dataset",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "UNIVERSE"
+  },
+  {
+    "id": "topsp500",
+    "description": "20140630",
+    "dataset_id": "univ1",
+    "dataset_name": "Universe Dataset",
+    "category_id": "pv",
+    "category_name": "Price Volume",
+    "region": "USA",
+    "delay": 1,
+    "universe": "TOP3000",
+    "type": "UNIVERSE"
+  }
+]