python 3.9 and zipline 1.4.1

.github/workflows/ci-ubuntu.yml

@@ -15,19 +15,30 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.6]
+        python-version: [3.9]
 
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v2
       with:
         python-version: ${{ matrix.python-version }}
+
+    - name: Install TA-Lib
+      run: |
+        # Insatll talib
+        cd /tmp && \
+        curl -L https://downloads.sourceforge.net/project/ta-lib/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz | \
+        tar xvz && \
+        cd /tmp/ta-lib && \
+        ./configure --prefix=/usr && \
+        make && \
+        sudo make install
+
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest
-        pip install pandas==0.22.0 numpy==1.19.4 scipy==1.5.4 pandas-datareader==0.8.1
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
         python setup.py install
     - name: Lint with flake8

Pipfile

@@ -6,7 +6,7 @@ name = "pypi"
 [packages]
 alpaca-trade-api = ">=0.37"
 iexfinance = ">=0.4.1"
-zipline = "==1.3.0"
+zipline-reloaded = "*"
 numpy = "*"
 
 [dev-packages]
@@ -15,4 +15,4 @@ pytest-cov = "*"
 flake8 = "*"
 
 [requires]
-python_version = "3.6"
+python_version = "3.9"

pipeline_live/data/alpaca/pricing_loader.py

@@ -1,6 +1,7 @@
 import numpy as np
 import logbook
 import pandas as pd
+from interface import implements
 
 from zipline.lib.adjusted_array import AdjustedArray
 from zipline.pipeline.loaders.base import PipelineLoader
@@ -13,17 +14,16 @@
 log = logbook.Logger(__name__)
 
 
-class USEquityPricingLoader(PipelineLoader):
+class USEquityPricingLoader(implements(PipelineLoader)):
     """
     PipelineLoader for US Equity Pricing data
     """
 
     def __init__(self):
         cal = get_calendar('NYSE')
-
         self._all_sessions = cal.all_sessions
 
-    def load_adjusted_array(self, columns, dates, symbols, mask):
+    def load_adjusted_array(self, domain, columns, dates, sids, mask):
         # load_adjusted_array is called with dates on which the user's algo
         # will be shown data, which means we need to return the data that would
         # be known at the start of each date.  We assume that the latest data
@@ -42,7 +42,7 @@ def load_adjusted_array(self, columns, dates, symbols, mask):
         prices = alpaca.get_stockprices(chart_range)
 
         dfs = []
-        for symbol in symbols:
+        for symbol in sids:
             if symbol not in prices:
                 df = pd.DataFrame(
                     {c.name: c.missing_value for c in columns},

pipeline_live/data/iex/fundamentals_loader.py

@@ -1,15 +1,15 @@
 import logging
 
 import numpy as np
-
+from interface import implements
 from pipeline_live.data.sources import iex
 from zipline.pipeline.loaders.base import PipelineLoader
 from zipline.utils.numpy_utils import object_dtype
 
 log = logging.getLogger(__name__)
 
 
-class IEXEventLoader(PipelineLoader):
+class IEXEventLoader(implements(PipelineLoader)):
 
     def _safe_flat_getter(self, symbol, symbols, column):
         data = symbols.get(symbol, None)
@@ -19,29 +19,29 @@ def _safe_flat_getter(self, symbol, symbols, column):
         return out
 
 
-    def load_adjusted_array(self, columns, dates, symbols, mask):
+    def load_adjusted_array(self, domain, columns, dates, sids, mask):
         symbol_dict = self._load()
         out = {}
         for c in columns:
             data = np.array([
                 self._safe_flat_getter(symbol, symbol_dict, c)
-                for symbol in symbols
+                for symbol in sids
             ], dtype=c.dtype)
             if c.dtype == object_dtype:
                 data[data == None] = c.missing_value  # noqa
             out[c] = np.tile(data, (len(dates), 1))
         return out
 
 
-class IEXBaseLoader(PipelineLoader):
+class IEXBaseLoader(implements(PipelineLoader)):
 
-    def load_adjusted_array(self, columns, dates, symbols, mask):
+    def load_adjusted_array(self, domain, columns, dates, sids, mask):
         symbol_dict = self._load()
         out = {}
         for c in columns:
             data = np.array([
                 symbol_dict.get(symbol, {}).get(c.name, c.missing_value)
-                for symbol in symbols
+                for symbol in sids
             ], dtype=c.dtype)
             if c.dtype == object_dtype:
                 data[data == None] = c.missing_value  # noqa

pipeline_live/data/iex/pricing_loader.py

@@ -1,6 +1,7 @@
 import numpy as np
 import logbook
 import pandas as pd
+from interface import implements
 
 from zipline.lib.adjusted_array import AdjustedArray
 from zipline.pipeline.loaders.base import PipelineLoader
@@ -13,7 +14,7 @@
 log = logbook.Logger(__name__)
 
 
-class USEquityPricingLoader(PipelineLoader):
+class USEquityPricingLoader(implements(PipelineLoader)):
     """
     PipelineLoader for US Equity Pricing data
     """
@@ -23,7 +24,7 @@ def __init__(self):
 
         self._all_sessions = cal.all_sessions
 
-    def load_adjusted_array(self, columns, dates, symbols, mask):
+    def load_adjusted_array(self, domain, columns, dates, sids, mask):
         # load_adjusted_array is called with dates on which the user's algo
         # will be shown data, which means we need to return the data that would
         # be known at the start of each date.  We assume that the latest data
@@ -52,7 +53,7 @@ def load_adjusted_array(self, columns, dates, symbols, mask):
         prices = iex.get_stockprices(chart_range)
 
         dfs = []
-        for symbol in symbols:
+        for symbol in sids:
             if symbol not in prices:
                 df = pd.DataFrame(
                     {c.name: c.missing_value for c in columns},

pipeline_live/data/polygon/fundamentals_loader.py

@@ -1,20 +1,20 @@
 import numpy as np
-
+from interface import implements
 from zipline.pipeline.loaders.base import PipelineLoader
 
 from pipeline_live.data.sources import polygon
 
 
-class PolygonCompanyLoader(PipelineLoader):
+class PolygonCompanyLoader(implements(PipelineLoader)):
 
-    def load_adjusted_array(self, columns, dates, symbols, mask):
+    def load_adjusted_array(self, domain, columns, dates, sids, mask):
 
         company = polygon.company()
         out = {}
         for c in columns:
             data = [
                 company.get(symbol, {}).get(c.name, c.missing_value)
-                for symbol in symbols
+                for symbol in sids
             ]
             out[c] = np.tile(np.array(data, dtype=c.dtype), (len(dates), 1))
 

setup.py

@@ -25,14 +25,11 @@
     keywords='financial,zipline,pipeline,stock,screening,api,trade',
     packages=find_packages(),
     install_requires=[
-        'pandas==0.22.0',
-        'numpy==1.19.4',
-        'scipy<=1.6.0',
-        'pandas-datareader<=0.8.1',
+        'pandas-datareader>=0.8.1',
         'lxml>=4.6.2',
         'alpaca-trade-api>=0.52.0',
         'iexfinance>=0.4.1,<0.5.0',
-        'zipline==1.3.0',
+        'zipline-reloaded>=2',
     ],
     tests_require=[
         'pytest',

tests/test_alpaca.py

@@ -10,10 +10,11 @@ def test_pricing_loader(refdata, alpaca_tradeapi, data_path):
     mock_tradeapi.get_barset(alpaca_tradeapi)
 
     loader = USEquityPricing.get_loader()
+    domain = None
     columns = [USEquityPricing.close]
     dates = [pd.Timestamp('2018-08-22', tz='UTC')]
     symbols = ['AA']
     mask = np.zeros((1, 1), dtype='bool')
-    out = loader.load_adjusted_array(columns, dates, symbols, mask)
+    out = loader.load_adjusted_array(domain, columns, dates, symbols, mask)
 
-    assert out[USEquityPricing.close]._data.shape == (1, 1)
+    assert out[USEquityPricing.close]._data.shape == (1, 1)

tests/test_factors.py

@@ -3,4 +3,8 @@
 
 
 def test_factors():
-    assert factors.AverageDollarVolume.inputs[0] == USEquityPricing.close
+    factor = factors.AverageDollarVolume(
+        window_length=30,
+        inputs=[USEquityPricing.close, USEquityPricing.volume])
+
+    assert factor.inputs[0] == USEquityPricing.close

tests/test_iex.py

@@ -12,10 +12,11 @@ def test_IEXKeyStats(refdata, stocks, data_path):
     mock_iex.get_available_symbols(refdata)
     mock_iex.get_key_stats(stocks)
 
+    domains = None
     marketcap = IEXKeyStats.marketcap
     loader = marketcap.dataset.get_loader()
     date = pd.Timestamp('2018-08-20', tz='utc')
-    out = loader.load_adjusted_array([marketcap], [date], ['AA'], [])
+    out = loader.load_adjusted_array(domains, [marketcap], [date], ['AA'], [])
 
     assert out[marketcap][0][0] != 0.0
 
@@ -26,9 +27,10 @@ def test_pricing_loader(refdata, stocks, data_path):
 
     loader = USEquityPricing.get_loader()
     columns = [USEquityPricing.close]
+    domains = None
     dates = [pd.Timestamp('2018-08-22', tz='UTC')]
     symbols = ['AA']
     mask = np.zeros((1, 1), dtype='bool')
-    out = loader.load_adjusted_array(columns, dates, symbols, mask)
+    out = loader.load_adjusted_array(domains, columns, dates, symbols, mask)
 
     assert out[USEquityPricing.close]._data.shape == (1, 1)

tests/test_polygon.py

@@ -11,10 +11,11 @@ def test_PolygonCompany(tradeapi, data_path):
     mock_tradeapi.list_assets(tradeapi)
     mock_tradeapi.list_companies(tradeapi)
 
+    domains = None
     marketcap = PolygonCompany.marketcap
     loader = marketcap.dataset.get_loader()
     date = pd.Timestamp('2018-08-20', tz='utc')
-    out = loader.load_adjusted_array([marketcap], [date], ['AA'], [])
+    out = loader.load_adjusted_array(domains, [marketcap], [date], ['AA'], [])
 
     assert out[marketcap][0][0] > 10e6