fix pandas 2 compat

coin2086/pnl.py

@@ -8,7 +8,7 @@
 
 
 def add_portfolio_purchase_price(trades, initial_purchase_price):
-    trades["portfolio_purchase_price"] = 0
+    trades["portfolio_purchase_price"] = 0.0
     trades.loc[trades["trade_side"] == "BUY", "portfolio_purchase_price"] = (
         trades["amount"] + trades["fee"]
     )

coin2086/pricedownload.py

@@ -18,7 +18,8 @@ class PriceDownloader(abc.ABC):
     def download_price(self, crypto, dtime):
         pass
 
-    @abc.abstractproperty
+    @property
+    @abc.abstractmethod
     def supported_crypto_list(self):
         pass
 

coin2086/valuation.py

@@ -96,6 +96,8 @@ def unstack_portfolio_composition(trades, sales, initial_portfolio):
     portfolio = portfolio.drop(columns=["sign", "trade_side"])
     # Unstack the composition of the portofolio after each transaction
     portfolio = portfolio.set_index("cryptocurrency", append=True).unstack().fillna(0)
+    # Ensure column names are preserved for pandas 2.x compatibility
+    portfolio.columns.names = [None, 'cryptocurrency']
     # We need the valuation of the portoflio *before* each transaction
     # so shift by one
     portfolio["quantity"] = portfolio["quantity"].cumsum().shift(1, fill_value=0)
@@ -105,10 +107,13 @@ def unstack_portfolio_composition(trades, sales, initial_portfolio):
 
 
 def merge_rates_and_valuate(portfolio):
-    portfolio = portfolio.stack()
+    # Stack the second level (cryptocurrency) into the index
+    portfolio = portfolio.stack(level=1, future_stack=True)
     portfolio["ref_price"] = portfolio["sell_price"].fillna(portfolio["public_price"])
     portfolio["value"] = portfolio["ref_price"] * portfolio["quantity"]
-    portfolio = portfolio.unstack()
+    # Unstack back and explicitly set the column names
+    portfolio = portfolio.unstack(level=-1)
+    portfolio.columns.names = [None, 'cryptocurrency']
     portfolio["value", "TOTAL"] = portfolio["value"].sum(axis=1)
     return portfolio
 
@@ -117,6 +122,8 @@ def add_sell_prices(portfolio, sales):
     sell_prices = sales[["cryptocurrency", "price"]]
     sell_prices = sell_prices.rename(columns={"price": "sell_price"})
     sell_prices = sell_prices.set_index("cryptocurrency", append=True).unstack()
+    # Ensure column names are preserved for pandas 2.x compatibility
+    sell_prices.columns.names = [None, 'cryptocurrency']
     return portfolio.join(sell_prices, how="outer")
 
 

pyproject.toml

@@ -18,7 +18,7 @@ classifiers = [
 
 [tool.poetry.dependencies]
 python = ">=3.6.2,<4.0"
-pandas = "^1.1"
+pandas = ">=1.1,<3.0"
 requests = "^2.10"
 
 [tool.poetry.dev-dependencies]

tests/reference_data/interleaved_multiyear_trades_detailed_pnl.csv

@@ -1,6 +1,6 @@
 ,datetime,trade_side,cryptocurrency,quantity,amount,fee,amount_net,portfolio_value,portfolio_purchase_price,purchase_price_fraction,purchase_price_fraction_sum,portfolio_purchase_price_net,pnl
 1,2019-11-14 19:50:00,SELL,BTC,0.5,3922.44,19.6122,3902.8278,7844.88,7185.1269,3592.56345,0.0,7185.1269,310.2643499999999
 4,2020-09-05 16:50:00,SELL,BTC,1.0,8722.7,43.6135,8679.086500000001,23310.95,27780.330599999998,9050.795292311339,3592.56345,24187.767149999996,-371.7087923113377
-5,2020-09-08 12:40:00,SELL,ETH,5.0,1425.35,7.12675,1418.22325,14189.210000000001,27780.330599999998,1520.5556079130922,12643.358742311339,15136.971857688659,-102.33235791309221
+5,2020-09-08 12:40:00,SELL,ETH,5.0,1425.35,7.1267499999999995,1418.22325,14189.210000000001,27780.330599999998,1520.5556079130922,12643.358742311339,15136.971857688659,-102.33235791309221
 6,2020-12-20 09:10:00,SELL,BTC,0.25,4805.975,24.029875000000004,4781.945125,28835.850000000002,27780.330599999998,2269.4027082959274,14163.914350224431,13616.416249775566,2512.542416704073
 7,2021-03-13 23:40:00,SELL,BTC,0.25,12506.2925,62.5314625,12443.7610375,62531.462499999994,27780.330599999998,2269.402708295928,16433.31705852036,11347.013541479639,10174.358329204073

tests/reference_data/interleaved_trades_detailed_pnl.csv

@@ -1,4 +1,4 @@
 ,datetime,trade_side,cryptocurrency,quantity,amount,fee,amount_net,portfolio_value,portfolio_purchase_price,purchase_price_fraction,purchase_price_fraction_sum,portfolio_purchase_price_net,pnl
 2,2020-09-05 16:50:00,SELL,BTC,0.5,4361.35,21.80675,4339.543250000001,10226.900000000001,11286.471599999999,4813.213477462378,0.0,11286.471599999999,-473.6702274623776
-3,2020-09-08 12:40:00,SELL,ETH,5.0,1425.35,7.12675,1418.22325,5679.969999999999,11286.471599999999,1624.4202812618723,4813.213477462378,6473.258122537621,-206.19703126187233
+3,2020-09-08 12:40:00,SELL,ETH,5.0,1425.35,7.1267499999999995,1418.22325,5679.969999999999,11286.471599999999,1624.4202812618723,4813.213477462378,6473.258122537621,-206.19703126187233
 6,2020-12-21 09:30:00,SELL,BTC,1.0,19531.69,97.65845,19434.03155,31934.784999999996,22507.658399999997,9828.616024368384,6437.633758724251,16070.024641275746,9605.415525631615

tests/test_non_regression.py

@@ -69,7 +69,9 @@ def test_compute_pnl():
     )
     pnl_declare_ref = pd.read_csv(pnl_declare_path, index_col=0)
     # Convert 2nd columns (Date de la cession to datetime64s)
-    pnl_declare_ref.iloc[:, 1] = pd.to_datetime(pnl_declare_ref.iloc[:, 1])
+    # Use column name instead of iloc for pandas 2.x compatibility
+    date_col_name = pnl_declare_ref.columns[1]
+    pnl_declare_ref[date_col_name] = pd.to_datetime(pnl_declare_ref[date_col_name])
     pnl_declare, total_pnl = coin2086.compute_taxable_pnls(trades, 2020)
     print(pnl_declare)
     pd.testing.assert_frame_equal(pnl_declare, pnl_declare_ref)