New alphas

Author: Breta01

.github/workflows/python-test.yml

@@ -18,10 +18,10 @@ jobs:
 
     steps:
     - uses: actions/checkout@v4
-    - name: Set up Python 3.10
+    - name: Set up Python 3.11
       uses: actions/setup-python@v3
       with:
-        python-version: "3.10"
+        python-version: "3.11"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

brain/alphas/alpha_27.py

@@ -0,0 +1,17 @@
+# Code
+code = """
+-winsorize(ts_delta(ts_mean(mdl77_fa_pedwf_cf, 20), 110) / ts_mean(fnd6_newa2v1300_wcap, 20) * rank(ts_mean(est_ptp, 20)))
+"""
+
+# Simulation Settings
+config = {
+    "region": "USA",
+    "universe": "TOP500",
+    "decay": 8,
+    "delay": 1,
+    "truncation": 0.01,
+    "neutralization": "MARKET",
+    "pasteurization": "ON",
+    "nan_handling": "OFF",
+    "unit_handling": "VERIFY",
+}

brain/alphas/alpha_28.py

@@ -0,0 +1,17 @@
+# Code
+code = """
+-ts_zscore(ts_sum(mdl77_fa_vefcfmtt + snt1_d1_stockrank, 20), 20)
+"""
+
+# Simulation Settings
+config = {
+    "region": "USA",
+    "universe": "TOP3000",
+    "decay": 15,
+    "delay": 1,
+    "truncation": 0.01,
+    "neutralization": "MARKET",
+    "pasteurization": "ON",
+    "nan_handling": "OFF",
+    "unit_handling": "VERIFY",
+}

brain/search_algorithm.py

@@ -26,7 +26,7 @@ def get_score(alpha_id):
         return (
             alphas_dict[alpha_id].fitness
             + 1.5 * alphas_dict[alpha_id].sharpe
-            - decay_hyperbolic(alphas_dict[alpha_id].print_counter, gamma=0.05, delta=0.1)
+            - decay_hyperbolic(alphas_dict[alpha_id].print_counter, gamma=0.01, delta=0.02)
         )
 
     n_largest = 10

brain/tools/alpha_ideas.py

@@ -158,23 +158,6 @@ def ranked_mean():
 """
 
 
-def scale_and_corr():
-    return """
-Alpha ideas:
-`scale(ts_mean({data_field}, 5) - {data_field}) + 5 * scale(ts_corr({data_field}, ts_delay({data_field}, 5), 40)`
-
-First part calculates the difference between the mean of the {data_field} over the last 20 periods
-and the current value of {data_field}. Second part calculates the correlation between
-the {data_field} and its value delayed by 5 periods, scaled by 20.
-Replace `{data_field}` with any data field you want to use.
-Numbers can be changed to any other values.
-
-
-For example:
-`scale(ts_mean(close, 5) - close) + 5 * scale(ts_corr(close, ts_delay(close, 5), 40)`
-"""
-
-
 def group_op():
     return """
 Alpha idea:
@@ -214,6 +197,7 @@ def regression():
 `ts_regression({field1}, {field2}, 40)`
 `ts_corr({field1}, {field2}, 50)`
 `ts_regression(ts_regression({field1}, {field2}, 30), ts_std_dev(returns, 30), 30)`
+`ts_covariance({field1}, {field2}, 30)`
 
 Use `ts_regression` or `ts_corr` to find the relationship between two data fields.
 Replace `{field1}` and `{field2}` with any data fields you like.
@@ -288,7 +272,6 @@ def get_alpha_idea():
         # random_alphas,
         conditional_alpha,
         ranked_mean,
-        scale_and_corr,
         group_op,
         use_rank,
         regression,

brain/tools/datafields.py

@@ -56,7 +56,7 @@ def search_datafields(datafield: str, config: RunnableConfig) -> str:
 
 def get_random_datafields(config: RunnableConfig) -> str:
     """Get a random datafields."""
-    global INDEX, DELAY_0, DELAY_1
+    global INDEX
     conf = get_universe_config(config)
 
     key = (conf["delay"], conf["universe"])