fixes

configuration/crypto.cfg

@@ -2,9 +2,9 @@
     "security_type": "CRYPTO",
     "data_directory": "./data/crypto",
     "datafiles": [
-        "20250519.mktdata.ohlcv.db"
+        "20250528.mktdata.ohlcv.db"
     ],
-    "db_table_name": "bnbspot_ohlcv_1min",
+    "db_table_name": "md_1min_bars",
     "exchange_id": "BNBSPOT",
     "instrument_id_pfx": "PAIR-",
     "instruments": [

requirements.txt

@@ -0,0 +1,218 @@
+aiohttp>=3.8.4
+aiosignal>=1.3.1
+apt-clone>=0.2.1
+apturl>=0.5.2
+async-timeout>=4.0.2
+attrs>=21.2.0
+beautifulsoup4>=4.10.0
+black>=23.3.0
+blinker>=1.4
+Brlapi>=0.8.3
+ccsm>=0.9.14.1
+certifi>=2020.6.20
+chardet>=4.0.0
+charset-normalizer>=3.1.0
+click>=8.0.3
+colorama>=0.4.4
+command-not-found>=0.3
+compizconfig-python>=0.9.14.1
+configobj>=5.0.6
+cryptography>=3.4.8
+cupshelpers>=1.0
+dbus-python>=1.2.18
+defer>=1.0.6
+distro>=1.7.0
+docker>=5.0.3
+docker-compose>=1.29.2
+dockerpty>=0.4.1
+docopt>=0.6.2
+eyeD3>=0.8.10
+filelock>=3.6.0
+frozenlist>=1.3.3
+grpcio>=1.30.2
+html5lib>=1.1
+httplib2>=0.20.2
+idna>=3.3
+ifaddr>=0.1.7
+IMDbPY>=2021.4.18
+importlib-metadata>=4.6.4
+iotop>=0.6
+jeepney>=0.7.1
+jsonschema>=3.2.0
+keyring>=23.5.0
+launchpadlib>=1.10.16
+lazr.restfulclient>=0.14.4
+lazr.uri>=1.0.6
+louis>=3.20.0
+lxml>=4.8.0
+Mako>=1.1.3
+Markdown>=3.3.6
+MarkupSafe>=2.0.1
+meld>=3.20.4
+more-itertools>=8.10.0
+multidict>=6.0.4
+mypy>=0.942
+mypy-extensions>=0.4.3
+netaddr>=0.8.0
+netifaces>=0.11.0
+oauthlib>=3.2.0
+onboard>=1.4.1
+packaging>=23.1
+pathspec>=0.11.1
+pexpect>=4.8.0
+Pillow>=9.0.1
+platformdirs>=3.2.0
+protobuf>=3.12.4
+psutil>=5.9.0
+ptyprocess>=0.7.0
+pycairo>=1.20.1
+pycups>=2.0.1
+pycurl>=7.44.1
+pyelftools>=0.27
+Pygments>=2.11.2
+PyGObject>=3.42.1
+PyICU>=2.8.1
+PyJWT>=2.3.0
+PyNaCl>=1.5.0
+pyparsing>=2.4.7
+pyparted>=3.11.7
+pyrsistent>=0.18.1
+python-apt>=2.4.0+ubuntu4
+python-debian>=0.1.43+ubuntu1.1
+python-dotenv>=0.19.2
+python-magic>=0.4.24
+python-xapp>=2.2.2
+python-xlib>=0.29
+pyxdg>=0.27
+PyYAML>=5.4.1
+remarkable>=1.87
+reportlab>=3.6.8
+requests>=2.25.1
+requests-file>=1.5.1
+SecretStorage>=3.3.1
+setproctitle>=1.2.2
+six>=1.16.0
+soupsieve>=2.3.1
+ssh-import-id>=5.11
+statsmodels>=0.14.4
+systemd-python>=234
+texttable>=1.6.4
+tldextract>=3.1.2
+tomli>=1.2.2
+typed-ast>=1.4.3
+types-aiofiles>=0.1
+types-annoy>=1.17
+types-appdirs>=1.4
+types-atomicwrites>=1.4
+types-aws-xray-sdk>=2.8
+types-babel>=2.9
+types-backports-abc>=0.5
+types-backports.ssl-match-hostname>=3.7
+types-beautifulsoup4>=4.10
+types-bleach>=4.1
+types-boto>=2.49
+types-braintree>=4.11
+types-cachetools>=4.2
+types-caldav>=0.8
+types-certifi>=2020.4
+types-characteristic>=14.3
+types-chardet>=4.0
+types-click>=7.1
+types-click-spinner>=0.1
+types-colorama>=0.4
+types-commonmark>=0.9
+types-contextvars>=0.1
+types-croniter>=1.0
+types-cryptography>=3.3
+types-dataclasses>=0.1
+types-dateparser>=1.0
+types-DateTimeRange>=0.1
+types-decorator>=0.1
+types-Deprecated>=1.2
+types-docopt>=0.6
+types-docutils>=0.17
+types-editdistance>=0.5
+types-emoji>=1.2
+types-entrypoints>=0.3
+types-enum34>=1.1
+types-filelock>=3.2
+types-first>=2.0
+types-Flask>=1.1
+types-freezegun>=1.1
+types-frozendict>=0.1
+types-futures>=3.3
+types-html5lib>=1.1
+types-httplib2>=0.19
+types-humanfriendly>=9.2
+types-ipaddress>=1.0
+types-itsdangerous>=1.1
+types-JACK-Client>=0.1
+types-Jinja2>=2.11
+types-jmespath>=0.10
+types-jsonschema>=3.2
+types-Markdown>=3.3
+types-MarkupSafe>=1.1
+types-mock>=4.0
+types-mypy-extensions>=0.4
+types-mysqlclient>=2.0
+types-oauthlib>=3.1
+types-orjson>=3.6
+types-paramiko>=2.7
+types-Pillow>=8.3
+types-polib>=1.1
+types-prettytable>=2.1
+types-protobuf>=3.17
+types-psutil>=5.8
+types-psycopg2>=2.9
+types-pyaudio>=0.2
+types-pycurl>=0.1
+types-pyfarmhash>=0.2
+types-Pygments>=2.9
+types-PyMySQL>=1.0
+types-pyOpenSSL>=20.0
+types-pyRFC3339>=0.1
+types-pysftp>=0.2
+types-pytest-lazy-fixture>=0.6
+types-python-dateutil>=2.8
+types-python-gflags>=3.1
+types-python-nmap>=0.6
+types-python-slugify>=5.0
+types-pytz>=2021.1
+types-pyvmomi>=7.0
+types-PyYAML>=5.4
+types-redis>=3.5
+types-requests>=2.25
+types-retry>=0.9
+types-selenium>=3.141
+types-Send2Trash>=1.8
+types-setuptools>=57.4
+types-simplejson>=3.17
+types-singledispatch>=3.7
+types-six>=1.16
+types-slumber>=0.7
+types-stripe>=2.59
+types-tabulate>=0.8
+types-termcolor>=1.1
+types-toml>=0.10
+types-toposort>=1.6
+types-ttkthemes>=3.2
+types-typed-ast>=1.4
+types-tzlocal>=0.1
+types-ujson>=0.1
+types-vobject>=0.9
+types-waitress>=0.1
+types-Werkzeug>=1.0
+types-xxhash>=2.0
+typing-extensions>=3.10.0.2
+ubuntu-drivers-common>=0.0.0
+ufw>=0.36.1
+Unidecode>=1.3.3
+urllib3>=1.26.5
+wadllib>=1.3.6
+webencodings>=0.5.1
+websocket-client>=1.2.3
+xdg>=5
+xkit>=0.0.0
+yarl>=1.9.1
+youtube-dl>=2021.12.17
+zipp>=1.0.0

src/pt_backtest.py

@@ -6,7 +6,6 @@ from typing import Any, Dict, List
 
 import pandas as pd
 
-from strategies import SlidingFitStrategy, StaticFitStrategy
 from tools.data_loader import load_market_data
 from tools.trading_pair import TradingPair
 from results import BacktestResult
@@ -68,21 +67,21 @@ def main() -> None:
     )
     args = parser.parse_args()
 
-    CONFIG = load_config(args.config)
+    config: Dict = load_config(args.config)
 
     # Dynamically instantiate strategy class
-    strategy_class_name = CONFIG.get("strategy_class", "strategies.StaticFitStrategy")
+    strategy_class_name = config.get("strategy_class", "strategies.StaticFitStrategy")
     module_name, class_name = strategy_class_name.rsplit(".", 1)
     module = importlib.import_module(module_name)
-    STRATEGY = getattr(module, class_name)()
+    strategy = getattr(module, class_name)()
 
     # Initialize a dictionary to store all trade results
     all_results: Dict[str, Dict[str, Any]] = {}
-    bt_results = BacktestResult(config=CONFIG)
+    bt_results = BacktestResult(config=config)
 
     # Process each data file
-    price_column = CONFIG["price_column"]
+    price_column = config["price_column"]
-    for datafile in CONFIG["datafiles"]:
+    for datafile in config["datafiles"]:
         print(f"\n====== Processing {datafile} ======")
 
         # Clear the TRADES global dictionary and reset unrealized PnL for the new file
@@ -91,11 +90,11 @@ def main() -> None:
         # Process data for this file
         try:
             run_all_pairs(
-                config=CONFIG,
+                config=config,
                 datafile=datafile,
                 price_column=price_column,
                 bt_result=bt_results,
-                strategy=STRATEGY,
+                strategy=strategy,
             )
 
             # Store results with file name as key

src/tools/data_loader.py

@@ -1,9 +1,7 @@
-import sys
 import sqlite3
-from typing import Dict, Tuple
+from typing import Dict
 import pandas as pd
 
-from tools.trading_pair import TradingPair
 
 
 def load_sqlite_to_dataframe(db_path, query):
@@ -15,15 +13,15 @@ def load_sqlite_to_dataframe(db_path, query):
     except sqlite3.Error as excpt:
         print(f"SQLite error: {excpt}")
         raise
-    except Exception as e:
+    except Exception as excpt:
         print(f"Error: {excpt}")
-        raise
+        raise Exception() from excpt
     finally:
         if "conn" in locals():
             conn.close()
 
 
-def convert_time_to_UTC(value: str, timezone: str):
+def convert_time_to_UTC(value: str, timezone: str) -> str:
 
     from zoneinfo import ZoneInfo
     from datetime import datetime
@@ -32,12 +30,9 @@ def convert_time_to_UTC(value: str, timezone: str):
     local_dt = datetime.strptime(value, "%Y-%m-%d %H:%M:%S")
 
     zinfo = ZoneInfo(timezone)
-    result = local_dt.replace(tzinfo=zinfo)
+    result: datetime = local_dt.replace(tzinfo=zinfo).astimezone(ZoneInfo("UTC"))
 
-    result = result.astimezone(ZoneInfo("UTC"))
+    return result.strftime("%Y-%m-%d %H:%M:%S")
-    result = result.strftime("%Y-%m-%d %H:%M:%S")
-
-    return result
 
 
 def load_market_data(datafile: str, config: Dict) -> pd.DataFrame:
@@ -52,7 +47,7 @@ def load_market_data(datafile: str, config: Dict) -> pd.DataFrame:
 
     query = "select"
     if security_type == "CRYPTO":
-        query += " strftime('%Y-%m-%d %H:%M:%S', tstamp/1000000000, 'unixepoch') as tstamp"
+        query += " strftime('%Y-%m-%d %H:%M:%S', tstamp_ns/1000000000, 'unixepoch') as tstamp"
         query += ", tstamp as time_ns"
     else:
         query += " tstamp"

src/tools/trading_pair.py

@@ -1,7 +1,7 @@
 
 from typing import Any, Dict, List, Optional
-import pandas as pd
+import pandas as pd  #type:ignore
-from statsmodels.tsa.vector_ar.vecm import VECM
+from statsmodels.tsa.vector_ar.vecm import VECM #type:ignore
 
 class TradingPair:
     market_data_: pd.DataFrame
@@ -16,7 +16,7 @@ class TradingPair:
     testing_df_: Optional[pd.DataFrame]
 
     vecm_fit_: Optional[VECM]
-    
+
     user_data_: Dict[str, Any]
 
     def __init__(self, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str):
@@ -24,13 +24,13 @@ class TradingPair:
         self.symbol_b_ = symbol_b
         self.price_column_ = price_column
         self.market_data_ = self._transform_dataframe(market_data)[["tstamp"] + self.colnames()]
-        
+
         self.training_mu_ = None
         self.training_std_ = None
         self.training_df_ = None
         self.testing_df_ = None
         self.vecm_fit_ = None
-        
+
         self.user_data_ = {}
 
     def _transform_dataframe(self, df: pd.DataFrame):
@@ -41,7 +41,9 @@ class TradingPair:
         result_df: pd.DataFrame = pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
 
         # For each unique symbol, add a corresponding close price column
-        for symbol in df_selected["symbol"].unique():
+
+        symbols = df_selected["symbol"].unique()
+        for symbol in symbols:
             # Filter rows for this symbol
             df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(drop=True)
 
@@ -60,7 +62,7 @@ class TradingPair:
 
         return result_df
     def get_datasets(self, training_minutes: int, training_start_index: int = 0, testing_size: Optional[int] = None) -> None:
-        
+
         testing_start_index = training_start_index + training_minutes
         self.training_df_ = self.market_data_.iloc[training_start_index:testing_start_index, :].copy()
         self.training_df_ = self.training_df_.dropna().reset_index(drop=True)
@@ -108,6 +110,7 @@ class TradingPair:
 
         # print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80)
         self.fit_VECM()
+        assert self.training_df_ is not None and self.vecm_fit_ is not None
         diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
         self.training_mu_ = diseq_series.mean().iloc[0]
         self.training_std_ = diseq_series.std().iloc[0]
@@ -121,10 +124,12 @@ class TradingPair:
         return True
 
     def predict(self) -> None:
+        assert self.testing_df_ is not None
+        assert self.vecm_fit_ is not None
         predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_))
 
         # Convert prediction to a DataFrame for readability
-        # predicted_df = 
+        # predicted_df =
 
         self.predicted_df_ = pd.merge(
             self.testing_df_.reset_index(drop=True),
@@ -144,7 +149,7 @@ class TradingPair:
         self.predicted_df_ = self.predicted_df_.reset_index()
         return self.predicted_df_
 
-        
+
     def __repr__(self) ->str:
         return f"{self.symbol_a_} & {self.symbol_b_}"