feat: symbols_in_namespace() method in namespaces.py

Author: chilango74

README.md

@@ -4,7 +4,7 @@
         <img src="https://img.shields.io/badge/python-v3-brightgreen.svg"
             alt="python"></a> &nbsp;
     <a href="https://pypi.org/project/okama/">
-        <img src="https://img.shields.io/badge/pypi-v0.92-brightgreen.svg"
+        <img src="https://img.shields.io/badge/pypi-v0.93-brightgreen.svg"
             alt="pypi"></a> &nbsp;
     <a href="https://opensource.org/licenses/MIT">
         <img src="https://img.shields.io/badge/license-MIT-brightgreen.svg"
@@ -84,10 +84,13 @@ x.wealth_indexes.plot()
 ![](../images/images/readmi03.jpg?raw=true) 
 
 ### 2. Create a dividend stocks portfolio with base currency EUR
+
 ```python
+import okama.portfolio
+
 weights = [0.3, 0.2, 0.2, 0.2, 0.1]
 assets = ['T.US', 'XOM.US', 'FRE.XETR', 'SNW.XETR', 'LKOH.MOEX']
-pf = ok.Portfolio(assets, weights=weights, ccy='EUR')
+pf = okama.portfolio.Portfolio(assets, weights=weights, ccy='EUR')
 print(pf)
 ```
 ![](../images/images/readmi04.jpg?raw=true) 

main.py

@@ -1,8 +1,3 @@
 import okama as ok
-import pandas as pd
 
-x = ok.Portfolio(symbols=['RUB.FX', 'MCFTR.INDX'], ccy='RUB',
-                                      first_date='2015-01', last_date='2020-01', inflation=True)
-print(x.describe())
-print('\n\n*******************')
-print(pd.read_pickle('tests/data/portfolio_description.pkl'))
+print(ok.symbols_in_namespace('CBR').info)

notebooks/01 howto.ipynb

@@ -20,13 +20,16 @@
 
 """
 
-from okama.assets import Asset, AssetList, Portfolio
+from okama.assets import Asset, AssetList
+from okama.portfolio import Portfolio
 from okama.macro import Inflation, Rate
-from okama.frontier import EfficientFrontier
-from okama.frontier_reb import EfficientFrontierReb
+from okama.frontier.multi_period import EfficientFrontierReb
 from okama.plots import Plots
-from okama.data import QueryData, API, search, namespaces, assets_namespaces, macro_namespaces
+from okama.api.data_queries import QueryData
+from okama.api.search import search
+from okama.api.api_methods import API
+from okama.api.namespaces import namespaces, assets_namespaces, macro_namespaces, symbols_in_namespace
 from okama.helpers import Float, Frame, Rebalance, Date
 import okama.settings
 
-__version__ = '0.92'
+__version__ = '0.93'

okama/__init__.py

@@ -1,128 +1,6 @@
-from typing import Dict
-from functools import lru_cache
-
-from io import StringIO
-import json
-
 import requests
-import pandas as pd
-import numpy as np
-
-from .settings import default_ticker
-
-
-def search(search_string: str) -> json:
-    string_response = API.search(search_string)
-    return json.loads(string_response)
-
-
-@lru_cache()
-def get_namespaces():
-    string_response = API.get_namespaces()
-    return json.loads(string_response)
-
-
-@lru_cache()
-def get_assets_namespaces():
-    string_response = API.get_assets_namespaces()
-    return json.loads(string_response)
-
-
-@lru_cache()
-def get_macro_namespaces():
-    string_response = API.get_macro_namespaces()
-    return json.loads(string_response)
 
-
-@lru_cache()
-def no_dividends_namespaces():
-    string_response = API.get_no_dividends_namespaces()
-    return json.loads(string_response)
-
-
-class QueryData:
-    """
-    Set of methods to select a source and get_ts the data.
-    """
-
-    @staticmethod
-    def get_symbol_info(symbol: str) -> Dict[str, str]:
-        json_input = API.get_symbol_info(symbol)
-        return json.loads(json_input)
-
-    @staticmethod
-    def csv_to_series(csv_input: str, period: str) -> pd.Series:
-        ts = pd.read_csv(StringIO(csv_input),
-                         delimiter=',',
-                         index_col=0,
-                         parse_dates=[0],
-                         dtype={1: np.float64},
-                         engine='python')
-        if not ts.empty:
-            ts.index = ts.index.to_period(period.upper())
-            ts = ts.squeeze('columns')
-        return ts
-
-    @staticmethod
-    def get_macro_ts(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01') -> pd.Series:
-        """
-        Requests API for Macroeconomic indicators time series (monthly data).
-        - Inflation time series
-        - Bank rates time series
-        """
-        csv_input = API.get_macro(symbol=symbol, first_date=first_date, last_date=last_date)
-        return QueryData.csv_to_series(csv_input, period='M')
-
-    @staticmethod
-    def get_ror(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:
-        """
-        Requests API for rate of return time series.
-        """
-        csv_input = API.get_ror(symbol=symbol, first_date=first_date, last_date=last_date, period=period)
-        return QueryData.csv_to_series(csv_input, period)
-
-    @staticmethod
-    def get_nav(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:
-        """
-        NAV time series for funds (works for PIF namespace only).
-        """
-        csv_input = API.get_nav(symbol=symbol, first_date=first_date, last_date=last_date, period=period)
-        return QueryData.csv_to_series(csv_input, period=period)
-
-    @staticmethod
-    def get_close(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:
-        """
-        Gets 'close' time series for a ticker.
-        """
-        csv_input = API.get_close(symbol=symbol, first_date=first_date, last_date=last_date, period=period)
-        return QueryData.csv_to_series(csv_input, period)
-
-    @staticmethod
-    def get_adj_close(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:
-        """
-        Gets 'adjusted close' time series for a ticker.
-        """
-        csv_input = API.get_adjusted_close(symbol=symbol, first_date=first_date, last_date=last_date, period=period)
-        return QueryData.csv_to_series(csv_input, period)
-
-    @staticmethod
-    def get_dividends(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01',) -> pd.Series:
-        """
-        Dividends time series daily data (dividend payment day should be considered).
-        """
-        if symbol.split('.', 1)[-1] not in no_dividends_namespaces():
-            csv_input = API.get_dividends(symbol, first_date=first_date, last_date=last_date)
-            ts = QueryData.csv_to_series(csv_input, period='D')
-        else:
-            # make empty time series when no dividends
-            ts = pd.Series(dtype=float)
-            ts.rename(symbol, inplace=True)
-        return ts
-
-    @staticmethod
-    def get_live_price(symbol: str) -> float:
-        price = API.get_live_price(symbol)
-        return float(price)
+from ..settings import default_ticker, default_namespace
 
 
 class API:
@@ -240,6 +118,10 @@ def get_macro(cls,
     def get_namespaces(cls):
         return cls.connect(endpoint=cls.endpoint_namespaces, symbol='')
 
+    @classmethod
+    def get_symbols_in_namespace(cls, namespace: str = default_namespace):
+        return cls.connect(endpoint=cls.endpoint_namespaces, symbol=namespace)
+
     @classmethod
     def get_assets_namespaces(cls):
         return cls.connect(endpoint=cls.endpoint_assets_namespaces, symbol='')
@@ -263,8 +145,3 @@ def search(cls, search_string: str):
     @classmethod
     def get_live_price(cls, symbol: str):
         return cls.connect(endpoint=cls.endpoint_live_price, symbol=symbol)
-
-
-namespaces = get_namespaces()
-assets_namespaces = get_assets_namespaces()
-macro_namespaces = get_macro_namespaces()

okama/api/__init__.py

@@ -0,0 +1,95 @@
+from typing import Dict
+
+from io import StringIO
+import json
+
+import pandas as pd
+import numpy as np
+
+from .api_methods import API
+from .namespaces import no_dividends_namespaces
+
+
+class QueryData:
+    """
+    Set of methods to get symbols data from API.
+    """
+
+    @staticmethod
+    def get_symbol_info(symbol: str) -> Dict[str, str]:
+        json_input = API.get_symbol_info(symbol)
+        return json.loads(json_input)
+
+    @staticmethod
+    def csv_to_series(csv_input: str, period: str) -> pd.Series:
+        ts = pd.read_csv(StringIO(csv_input),
+                         delimiter=',',
+                         index_col=0,
+                         parse_dates=[0],
+                         dtype={1: np.float64},
+                         engine='python')
+        if not ts.empty:
+            ts.index = ts.index.to_period(period.upper())
+            ts = ts.squeeze('columns')
+        return ts
+
+    @staticmethod
+    def get_macro_ts(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01') -> pd.Series:
+        """
+        Requests API for Macroeconomic indicators time series (monthly data).
+        - Inflation time series
+        - Bank rates time series
+        """
+        csv_input = API.get_macro(symbol=symbol, first_date=first_date, last_date=last_date)
+        return QueryData.csv_to_series(csv_input, period='M')
+
+    @staticmethod
+    def get_ror(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:
+        """
+        Requests API for rate of return time series.
+        """
+        csv_input = API.get_ror(symbol=symbol, first_date=first_date, last_date=last_date, period=period)
+        return QueryData.csv_to_series(csv_input, period)
+
+    @staticmethod
+    def get_nav(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:
+        """
+        NAV time series for funds (works for PIF namespace only).
+        """
+        csv_input = API.get_nav(symbol=symbol, first_date=first_date, last_date=last_date, period=period)
+        return QueryData.csv_to_series(csv_input, period=period)
+
+    @staticmethod
+    def get_close(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:
+        """
+        Gets 'close' time series for a ticker.
+        """
+        csv_input = API.get_close(symbol=symbol, first_date=first_date, last_date=last_date, period=period)
+        return QueryData.csv_to_series(csv_input, period)
+
+    @staticmethod
+    def get_adj_close(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:
+        """
+        Gets 'adjusted close' time series for a ticker.
+        """
+        csv_input = API.get_adjusted_close(symbol=symbol, first_date=first_date, last_date=last_date, period=period)
+        return QueryData.csv_to_series(csv_input, period)
+
+    @staticmethod
+    def get_dividends(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01',) -> pd.Series:
+        """
+        Dividends time series daily data (dividend payment day should be considered).
+        """
+        if symbol.split('.', 1)[-1] not in no_dividends_namespaces():
+            csv_input = API.get_dividends(symbol, first_date=first_date, last_date=last_date)
+            ts = QueryData.csv_to_series(csv_input, period='D')
+        else:
+            # make empty time series when no dividends
+            ts = pd.Series(dtype=float)
+            ts.rename(symbol, inplace=True)
+        return ts
+
+    @staticmethod
+    def get_live_price(symbol: str) -> float:
+        price = API.get_live_price(symbol)
+        return float(price)

okama/data.py renamed to okama/api/api_methods.py

@@ -0,0 +1,44 @@
+import json
+from functools import lru_cache
+
+import pandas as pd
+
+from .api_methods import API
+from ..settings import default_namespace
+
+
+@lru_cache()
+def get_namespaces():
+    string_response = API.get_namespaces()
+    return json.loads(string_response)
+
+
+@lru_cache()
+def symbols_in_namespace(namespace: str = default_namespace):
+    string_response = API.get_symbols_in_namespace(namespace.upper())
+    list_of_symbols = json.loads(string_response)
+    df = pd.DataFrame(list_of_symbols[1:], columns=list_of_symbols[0])
+    return df.astype('string', copy=False)
+
+
+@lru_cache()
+def get_assets_namespaces():
+    string_response = API.get_assets_namespaces()
+    return json.loads(string_response)
+
+
+@lru_cache()
+def get_macro_namespaces():
+    string_response = API.get_macro_namespaces()
+    return json.loads(string_response)
+
+
+@lru_cache()
+def no_dividends_namespaces():
+    string_response = API.get_no_dividends_namespaces()
+    return json.loads(string_response)
+
+
+namespaces = get_namespaces()
+assets_namespaces = get_assets_namespaces()
+macro_namespaces = get_macro_namespaces()

okama/api/data_queries.py

@@ -0,0 +1,8 @@
+import json
+
+from .api_methods import API
+
+
+def search(search_string: str) -> json:
+    string_response = API.search(search_string)
+    return json.loads(string_response)