fix: pandas 3.0 compatibility (frequency alias migration)

.gitignore

@@ -15,6 +15,10 @@ price-data-new
 # Ignore frontend build
 **/**/build
 
+# Ignore virtual environments
+venv/
+.venv/
+
 # Ignore files related to API keys
 .env
 

pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     "jax>=0.4.27",
     "jaxlib",
     "numpy>=1.21.0",
-    "pandas>=1.3.6",
+    "pandas>=2.2",
     "flask",
     "flask-jwt-extended",
     "scipy",

quantammsim/core_simulator/windowing_utils.py

@@ -132,7 +132,7 @@ def raw_trades_to_trade_array(raw_trades, start_date_string, end_date_string, to
         pd.date_range(
             start=pd.to_datetime(start_date_string, format="%Y-%m-%d %H:%M:%S"),
             end=pd.to_datetime(end_date_string, format="%Y-%m-%d %H:%M:%S"),
-            freq="T",
+            freq="min",
         ).astype(int)
         // 10**6
     )

quantammsim/simulator_analysis_tools/finance/financial_analysis_charting.py

@@ -50,7 +50,7 @@ def plot_line_chart_from_results(
     series_dict = {}
     for result, series in zip(results_list, series_list):
         result_index = pd.date_range(
-            start=startDateString, periods=len(result), freq="T"
+            start=startDateString, periods=len(result), freq="min"
         )
         result_series = pd.Series(result, index=result_index)
         series_dict[series] = result_series

quantammsim/simulator_analysis_tools/finance/financial_analysis_functions.py

@@ -244,36 +244,36 @@ def calculate_drawdown_statistics(daily_returns, rf_values):
     weekly_max_drawdown = drawdown_weekly.min()
 
     # Monthly maximum drawdown
-    monthly_returns = daily_returns.resample("M").apply(lambda x: (1 + x).prod() - 1)
+    monthly_returns = daily_returns.resample("ME").apply(lambda x: (1 + x).prod() - 1)
     cumulative_monthly_returns = (1 + monthly_returns).cumprod()
     peak_monthly = cumulative_monthly_returns.cummax()
     drawdown_monthly = (cumulative_monthly_returns - peak_monthly) / peak_monthly
     monthly_max_drawdown = drawdown_monthly.min()
 
     daily_weekly_avg = calculate_average_daily_drawdown(daily_returns, "W")
-    daily_monthly_avg = calculate_average_daily_drawdown(daily_returns, "M")
+    daily_monthly_avg = calculate_average_daily_drawdown(daily_returns, "ME")
 
     daily_weekly_max = calculate_max_daily_drawdown(daily_returns, "W")
-    daily_monthly_max = calculate_max_daily_drawdown(daily_returns, "M")
+    daily_monthly_max = calculate_max_daily_drawdown(daily_returns, "ME")
 
     ulcer_index = calculate_ulcer_index(daily_returns)
 
     daily_weekly_ulcer = calcuate_period_ulcer_index(daily_returns, "W")
-    daily_monthly_ulcer = calcuate_period_ulcer_index(daily_returns, "M")
+    daily_monthly_ulcer = calcuate_period_ulcer_index(daily_returns, "ME")
 
     sterling = calculate_sterling_ratio(daily_returns, rf_values)
 
     daily_weekly_sterling = calcuate_period_sterling_index(
         daily_returns, rf_values, "W"
     )
     daily_monthly_sterling = calcuate_period_sterling_index(
-        daily_returns, rf_values, "M"
+        daily_returns, rf_values, "ME"
     )
 
     annualized_cDaR = calculate_cdar(daily_returns) * np.sqrt(365)
 
     weekly_cDaR = calculate_monthly_cdar(daily_returns, "W")
-    monthly_cDaR = calculate_monthly_cdar(daily_returns, "M")
+    monthly_cDaR = calculate_monthly_cdar(daily_returns, "ME")
 
     return {
         "Daily Returns Maximum Drawdown": abs(daily_max_drawdown),
@@ -500,7 +500,7 @@ def calcuate_period_sterling_index(daily_returns, rf_values, period):
     Parameters:
     daily_returns (np.array or pd.Series): Daily returns of the portfolio.
     rf_values (np.array or pd.Series): Daily risk-free rates.
-    period (str): Period for resampling (e.g., "M" for monthly).
+    period (str): Period for resampling (e.g., "ME" for monthly).
 
     Returns:
     np.array: Monthly Sterling Ratios.

quantammsim/simulator_analysis_tools/finance/param_financial_calculator.py

@@ -752,7 +752,7 @@ def fill_missing_values(target_directory, filename, output_filename):
 def calculate_daily_returns(minute_values, startDateString, name):
     # Create a pandas Series with minute-level values and a datetime index
     num_minutes = len(minute_values)
-    minute_index = pd.date_range(start=startDateString, periods=num_minutes, freq="T")
+    minute_index = pd.date_range(start=startDateString, periods=num_minutes, freq="min")
     minute_series = pd.Series(minute_values, index=minute_index)
     # Resample to daily frequency by taking the last value of each day
 
@@ -968,7 +968,7 @@ def retrieve_mc_param_financial_results(run_fingerprint, params, testEndDateStri
         minute_index = pd.date_range(
             start=run_fingerprint["startDateString"],
             periods=len(portfolio_result["value"]),
-            freq="T",
+            freq="min",
         )
         minute_series = pd.Series(portfolio_result["value"], index=minute_index)
         minute_series.to_csv("./results/portfolio_result_abs.csv")

quantammsim/utils/data_processing/binance_data_utils.py

@@ -199,7 +199,7 @@ def report_gaps(concatenated_df, gaps_output_file=None):
         pd.date_range(
             start=pd.to_datetime(start_unix, unit="ms"),
             end=pd.to_datetime(end_unix, unit="ms"),
-            freq="T",
+            freq="min",
         ).astype(int)
         // 10**9
     )

quantammsim/utils/data_processing/historic_data_utils.py

@@ -396,7 +396,7 @@ def update_historic_data_old(token, root):
         pd.date_range(
             start=pd.to_datetime(csvData.index.min(), unit="ms"),
             end=pd.to_datetime(csvData.index.max(), unit="ms"),
-            freq="T",
+            freq="min",
         ).astype(int)
         // 10**6
     )
@@ -554,7 +554,7 @@ def update_historic_data_old(token, root):
         pd.date_range(
             start=pd.to_datetime(hourly_data.index.min(), unit="ms"),
             end=pd.to_datetime(hourly_data.index.max(), unit="ms"),
-            freq="H",
+            freq="h",
         ).astype(int)
         // 10**6
     )
@@ -566,7 +566,7 @@ def update_historic_data_old(token, root):
         pd.date_range(
             start=pd.to_datetime(hourly_data.index.min(), unit="ms"),
             end=pd.to_datetime(hourly_data.index.max(), unit="ms"),
-            freq="T",
+            freq="min",
         ).astype(int)
         // 10**6
     )
@@ -987,7 +987,7 @@ def update_historic_data(token, root):
     agg_dict = {k: v for k, v in agg_dict.items() if k in concated_df_hourly.columns}
 
     # Perform resampling
-    hourly_data = concated_df_hourly.resample("1H").agg(agg_dict).reset_index()
+    hourly_data = concated_df_hourly.resample("1h").agg(agg_dict).reset_index()
 
     # Save hourly data
     hourly_data.to_csv(hourlyPath, index=False)

quantammsim/utils/data_processing/minute_daily_conversion_utils.py

@@ -20,7 +20,7 @@ def expand_daily_to_minute_data(daily_data, scale="ms"):
 
     # Create a date range with minute frequency
     minute_range = pd.date_range(
-        start=daily_data.index.min(), end=daily_data.index.max(), freq="T"
+        start=daily_data.index.min(), end=daily_data.index.max(), freq="min"
     )
 
     # Reindex the daily data to the minute range, forward filling the values

setup.py

@@ -8,7 +8,7 @@
         "jax>=0.4.27",
         "jaxlib",  # Required for JAX to work
         "numpy>=1.21.0",
-        "pandas>=1.3.0",
+        "pandas>=2.2",
         "flask",
         "flask-jwt-extended",
         "scipy",