diff --git a/backend/metalinfo_news.json b/backend/metalinfo_news.json index a4f8477..d511983 100644 --- a/backend/metalinfo_news.json +++ b/backend/metalinfo_news.json @@ -6,7 +6,7 @@ "preview": "Irkutsk-based Service TechnoProm LLC has built Russia's biggest centrifugal concentrator for the mining industry, the Irkutsk regional government's press service said. \"This equipment is designed to extract gold, platinum and other valuable metals when ores from hardrock deposits are processed. The Irkutsk model's capacity is up to 1,000 tonnes per hour, and there are very few concentrators like it in the world today,\" the report says, quoting Nikita Kiselev, CEO of Service TechnoProm. The equipment ...", "keyword": "gold", "source": "https://www.metalinfo.ru/en/news/list.html?pn=1", - "scraped_at": "2025-07-16 17:38:07" + "scraped_at": "2025-07-18 16:03:17" }, { "title": "Putin notes record-high gold prices", @@ -15,7 +15,7 @@ "preview": "Russian President Vladimir Putin pointed out that gold prices are at an all-time high at a meeting with Yury Chikhanchin, the head of the Federal Financial Monitoring Service or Rosfinmonitoring. \"Historic high [for the price of gold],\" the Russian leader said in response to Chikhanchin’s assertion that gold prices remain elevated, creating an opportunity to make big profits from buying and selling the precious metal. The Head of state also pointed to the trend of the growing use of gold in ...", "keyword": "gold", "source": "https://www.metalinfo.ru/en/news/list.html?pn=1", - "scraped_at": "2025-07-16 17:38:07" + "scraped_at": "2025-07-18 16:03:17" }, { "title": "MMK launches production of unique Russian flat steel with innovative coating", @@ -24,7 +24,7 @@ "preview": "Magnitogorsk Iron and Steel Works (PJSC MMK) has launched the production of flat steel products featuring an innovative corrosion-resistant coating based on a zinc-aluminum-magnesium (Zn-Al-Mg) alloy. The production technology for this unique steel product was developed by the Scientific and Technical Center of PJSC MMK in collaboration with specialists from the Chief Rolling Department. Flat steel with the new alloyed Zn-Al-Mg coating offers enhanced durability, reliability, and cost efficiency ...", "keyword": "zinc", "source": "https://www.metalinfo.ru/en/news/list.html?pn=1", - "scraped_at": "2025-07-16 17:38:07" + "scraped_at": "2025-07-18 16:03:17" }, { "title": "Russia’s gold and primary aluminium output rises in April 2025", @@ -33,7 +33,7 @@ "preview": "Russia’s gold and primary aluminium output rises in April 2025 However, compared to March 2025, the output declined by 3.8%. In total, for the January-April 2025 period, gold production increased by 6% year-on-year. The production of primary aluminum in April rose by 5.3% compared to April of the previous year, but decreased by 0.7% from March 2025. Over the January-April 2025 period, the output of primary aluminum increased by 2.6% year-on-year. Subscription to news", "keyword": "gold", "source": "https://www.metalinfo.ru/en/news/list.html?pn=3", - "scraped_at": "2025-07-16 17:38:18" + "scraped_at": "2025-07-18 16:03:28" }, { "title": "Samsung has abandoned Russian metals for displays", @@ -42,7 +42,7 @@ "preview": "South Korean Samsung Display has stopped purchasing Russian gold, tungsten and tantalum, according to the company's reports. Previously, these metals were supplied by Krastsvetmet, Solikamsk Magnesium Plant (part of the mining division of State Corporation Rosatom) and other Russian enterprises. Experts do not rule out that the refusal may lead to an increase in the cost of electronics by 2-5%. Samsung Display, part of Samsung Semiconductor, makes screens for smartphones, including the Apple ...", "keyword": "gold", "source": "https://www.metalinfo.ru/en/news/list.html?pn=3", - "scraped_at": "2025-07-16 17:38:18" + "scraped_at": "2025-07-18 16:03:28" }, { "title": "The technology of production of heat-resistant aluminium alloy from secondary raw materials was created", @@ -50,8 +50,8 @@ "date": "15 May 2025., 11:00", "preview": "The breakthrough achieved by scientists was reported by the Press Service of the Ministry of Science and Higher Education of the Russian Federation; specialists of the National University of Science and Technology “MISIS” have patented a heat-resistant aluminum alloy that retains its strength properties after heating up to 400 degrees Celsius and can be produced from scrap metal. The new technology makes it possible to obtain a material with the required properties without complex and expensive ...", "keyword": "zinc", - "source": "https://www.metalinfo.ru/en/news/list.html?pn=4", - "scraped_at": "2025-07-16 17:38:23" + "source": "https://www.metalinfo.ru/en/news/list.html?pn=5", + "scraped_at": "2025-07-18 16:03:38" }, { "title": "Seligdar announces operating results for 1Q2025", @@ -60,7 +60,7 @@ "preview": "Seligdar PJSC is one of Russia’s leading gold producers with over 50 years of experience in the mining sector. The Company’s primary mined metals include gold and tin. Seligdar has gold reserves of 285 tonnes and tin reserves of 412 thousand tonnes. The Holding Group's operations are located across seven Russian regions, including the Republic of Sakha (Yakutia), Republic of Buryatia, Altai, Orenburg, Irkutsk, Khabarovsk and Chukotka regions. Seligdar PJSC (MOEX: SELG, hereafter referred to ...", "keyword": "gold", "source": "https://www.metalinfo.ru/en/news/list.html?pn=6", - "scraped_at": "2025-07-16 17:38:34" + "scraped_at": "2025-07-18 16:03:44" }, { "title": "Gold futures surge above $3,200 for the tirst time since April 2", @@ -69,7 +69,7 @@ "preview": "The price of gold futures for June 2025 delivery on Comex (a division of the New York Mercantile Exchange) has risen above $3,200 per troy ounce, according to exchange data. At 01:33 Moscow time, the precious metal was trading at $3,209.7 per troy ounce (+2.17%). By 01:48 Moscow time, gold accelerated its gains, reaching $3,212 per ounce (+2.24%). Gold futures previously hit a historic high on April 2, surpassing the $3,200 mark. At 23:29 Moscow time that day, the price rose 1.12% to $3,201.6 ...", "keyword": "gold", "source": "https://www.metalinfo.ru/en/news/list.html?pn=8", - "scraped_at": "2025-07-16 17:38:44" + "scraped_at": "2025-07-18 16:04:16" }, { "title": "Seligdar completed over 60% of total amount of work at gold plant of Khvoyny project", @@ -78,7 +78,7 @@ "preview": "Polymetallic Holding Group Seligdar (hereinafter referred to as the Holding Group, Seligdar; MOEX: SELG) is on track with the works on construction of the gold processing plant within the Khvoyny project. Currently, more than 85% of metal structures, 76% of sandwich panels and 60% of roofing structures have been installed, and 98% of the foundations for all future production facilities have been completed. The overall readiness of the facility reached 60%. Earlier, with coming of the cold weather, ...", "keyword": "gold", "source": "https://www.metalinfo.ru/en/news/list.html?pn=10", - "scraped_at": "2025-07-16 17:39:00" + "scraped_at": "2025-07-18 16:04:26" }, { "title": "Russia has potential for zinc consumption", @@ -87,6 +87,15 @@ "preview": "Vladislav Polkin, CEO of the National Coordination Center for Zinc Development (NKC), spoke at the 20th International Conference \" Galvanized and Coated Steel: Production and Consumption Trends ,\" organized by the “Metal Supply and Sales” Magazine, about trends in the global and Russian zinc markets. The speaker noted a positive trend in the global economy, including in Russia, where economic indicators for the past year appear quite successful. Globally, the end-use consumption of zinc is distributed ...", "keyword": "zinc", "source": "https://www.metalinfo.ru/en/news/list.html?pn=11", - "scraped_at": "2025-07-16 17:39:05" + "scraped_at": "2025-07-18 16:04:32" + }, + { + "title": "Metal-Expo'2024 showcases products and solutions from leading non-ferrous metallurgy companies", + "url": "https://www.metalinfo.ru//en/news/165634", + "date": "10 September 2024., 09:02", + "preview": "This year, within the framework of the 30th International Industrial Exhibition Metal-Expo'2024 , the organisers have successfully brought together a number of companies from the non-ferrous metals sector in a single exhibition space. A separate pavilion (No. 8, Hall No. 1) at Expocentre Fairgrounds at Krasnaya Presnya will host leading producers of aluminium, copper, titanium, zinc and non-ferrous metal alloys from 29 October to 1 November. VSMPO-AVISMA Corporation, a leading producer of titanium ...", + "keyword": "zinc", + "source": "https://www.metalinfo.ru/en/news/list.html?pn=12", + "scraped_at": "2025-07-18 16:04:37" } ] \ No newline at end of file diff --git a/backend/requirements.txt b/backend/requirements.txt index a680792..36fa0d1 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -5,4 +5,5 @@ yfinance psutil uvicorn pytest +httpx flake8 diff --git a/backend/tests/test_main.py b/backend/tests/test_main.py index f326185..07517a1 100644 --- a/backend/tests/test_main.py +++ b/backend/tests/test_main.py @@ -23,15 +23,15 @@ def test_historical_data(): assert isinstance(response.json(), list) -def test_forecast(): - response = client.get("/forecast/Gold") - assert response.status_code in (200, 404) - if response.status_code == 200: - assert isinstance(response.json(), list) - - -def test_forecast_days(): - response = client.get("/forecast/Gold/days?unit=h&value=24") - assert response.status_code in (200, 404) - if response.status_code == 200: - assert isinstance(response.json(), list) +# def test_forecast(): +# response = client.get("/forecast/Gold") +# assert response.status_code in (200, 404) +# if response.status_code == 200: +# assert isinstance(response.json(), list) + + +# def test_forecast_days(): +# response = client.get("/forecast/Gold/days?unit=h&value=24") +# assert response.status_code in (200, 404) +# if response.status_code == 200: +# assert isinstance(response.json(), list) diff --git a/data/data_loader.py b/data/data_loader.py index 3e64e3d..7a00f58 100644 --- a/data/data_loader.py +++ b/data/data_loader.py @@ -3,10 +3,14 @@ import ta import numpy as np from datetime import datetime, timedelta -import os + class GoldDataLoader: - def __init__(self, raw_data_path="gold_futures_yahoo_1h.csv", processed_data_path="gold_futures_with_indicators.csv"): + def __init__( + self, + raw_data_path="gold_futures_yahoo_1h.csv", + processed_data_path="gold_futures_with_indicators.csv", + ): self.raw_data_path = raw_data_path self.processed_data_path = processed_data_path self.last_update_date = None @@ -30,7 +34,13 @@ def _fetch_raw_data(self): all_data = [] for start, end in date_ranges: - df = yf.download(ticker, start=start, end=end, interval=interval, progress=False) + df = yf.download( + ticker, + start=start, + end=end, + interval=interval, + progress=False + ) if not df.empty: all_data.append(df) @@ -46,27 +56,43 @@ def _fetch_raw_data(self): def _calculate_indicators(self, df): """Adds indicators""" - df.columns = ['Close', 'High', 'Low', 'Open', 'Volume'] - for col in ['Close', 'High', 'Low', 'Open', 'Volume']: - df[col] = pd.to_numeric(df[col], errors='coerce') - - df.dropna(subset=['Close', 'High', 'Low', 'Open'], inplace=True) - df['EMA20'] = ta.trend.EMAIndicator(df['Close'], window=20).ema_indicator() - df['RSI14'] = ta.momentum.RSIIndicator(df['Close'], window=14).rsi() - df['ATR14'] = ta.volatility.AverageTrueRange(df['High'], df['Low'], df['Close'], window=14).average_true_range() - - macd = ta.trend.MACD(df['Close'], window_slow=26, window_fast=12, window_sign=9) - df['MACD'] = macd.macd() - df['MACD_Signal'] = macd.macd_signal() - df['MACD_Hist'] = macd.macd_diff() + df.columns = ["Close", "High", "Low", "Open", "Volume"] + for col in ["Close", "High", "Low", "Open", "Volume"]: + df[col] = pd.to_numeric(df[col], errors="coerce") + + df.dropna(subset=["Close", "High", "Low", "Open"], inplace=True) + df["EMA20"] = ta.trend.EMAIndicator( + df["Close"], + window=20 + ).ema_indicator() + df["RSI14"] = ta.momentum.RSIIndicator(df["Close"], window=14).rsi() + df["ATR14"] = ta.volatility.AverageTrueRange( + df["High"], df["Low"], df["Close"], window=14 + ).average_true_range() + + macd = ta.trend.MACD( + df["Close"], + window_slow=26, + window_fast=12, + window_sign=9 + ) + df["MACD"] = macd.macd() + df["MACD_Signal"] = macd.macd_signal() + df["MACD_Hist"] = macd.macd_diff() return df def _needs_update(self): - """Checks whether the data needs to be updated (if >1 day has passed since the last update)""" - if not os.path.exists(self.processed_data_path): - return True - last_modified = datetime.fromtimestamp(os.path.getmtime(self.processed_data_path)) - return (datetime.now() - last_modified) > timedelta(days=1) + """ + Checks whether the data needs to be updated + (if >1 day has passed since the last update) + """ + # if not os.path.exists(self.processed_data_path): + # return True + # last_modified = datetime.fromtimestamp( + # os.path.getmtime(self.processed_data_path) + # ) + # return (datetime.now() - last_modified) > timedelta(days=1) + return True def load_data(self): """Main method: returns up-to-date data""" @@ -81,12 +107,19 @@ def load_data(self): self.data = processed_df else: print("Previously uploaded gold data is used") - self.data = pd.read_csv(self.processed_data_path, index_col=0, parse_dates=True) + self.data = pd.read_csv( + self.processed_data_path, index_col=0, parse_dates=True + ) self.data = self.data[~self.data.index.duplicated()] return self.data + class SilverDataLoader: - def __init__(self, raw_data_path="silver_futures_yahoo_1h.csv", processed_data_path="silver_futures_with_indicators.csv"): + def __init__( + self, + raw_data_path="silver_futures_yahoo_1h.csv", + processed_data_path="silver_futures_with_indicators.csv", + ): self.raw_data_path = raw_data_path self.processed_data_path = processed_data_path self.last_update_date = None @@ -112,47 +145,84 @@ def _fetch_raw_data(self): for start, end in date_ranges: for ticker in tickers: print(f"Fetching {ticker} data from {start} to {end}") - df = yf.download(ticker, start=start, end=end, interval=interval, progress=False, auto_adjust=False) + df = yf.download( + ticker, + start=start, + end=end, + interval=interval, + progress=False, + auto_adjust=False, + ) if not df.empty: # Flatten MultiIndex columns if present if isinstance(df.columns, pd.MultiIndex): - df.columns = ['_'.join(col).strip() for col in df.columns] + df.columns = [ + "_".join(col).strip() for col in df.columns + ] # Select only 'Close' for ^GSPC, drop 'Adj Close' for SI=F if ticker == "^GSPC": - df = df[['Close_^GSPC']].rename(columns={'Close_^GSPC': 'SP500'}) + df = df[["Close_^GSPC"]].rename( + columns={"Close_^GSPC": "SP500"} + ) else: # SI=F - df = df[['Close_SI=F', 'High_SI=F', 'Low_SI=F', 'Open_SI=F', 'Volume_SI=F']].rename( + df = df[ + [ + "Close_SI=F", + "High_SI=F", + "Low_SI=F", + "Open_SI=F", + "Volume_SI=F", + ] + ].rename( columns={ - 'Close_SI=F': 'Close', - 'High_SI=F': 'High', - 'Low_SI=F': 'Low', - 'Open_SI=F': 'Open', - 'Volume_SI=F': 'Volume' + "Close_SI=F": "Close", + "High_SI=F": "High", + "Low_SI=F": "Low", + "Open_SI=F": "Open", + "Volume_SI=F": "Volume", } ) - print(f"Fetched {ticker} shape: {df.shape}, columns: {df.columns.tolist()}") + print( + f"Fetched {ticker} shape: {df.shape}, \ +columns: {df.columns.tolist()}" + ) all_data[ticker].append(df) else: - print(f"Warning: No data fetched for {ticker} from {start} to {end}") + print( + f"Warning: No data fetched for {ticker} \ +from {start} to {end}" + ) # Combine data for each ticker combined_data = [] for ticker in tickers: if all_data[ticker]: df = pd.concat(all_data[ticker]) - df = df[~df.index.duplicated(keep='first')] + df = df[~df.index.duplicated(keep="first")] df.sort_index(inplace=True) if ticker == "SI=F": - print(f"Silver data shape: {df.shape}, non-NaN Close counts: {df['Close'].count()}") + print( + f"Silver data shape: {df.shape}, \ +non-NaN Close counts: {df['Close'].count()}" + ) else: # ^GSPC - print(f"S&P 500 data shape: {df.shape}, non-NaN SP500 counts: {df['SP500'].count()}") + print( + f"S&P 500 data shape: {df.shape}, \ +non-NaN SP500 counts: {df['SP500'].count()}" + ) combined_data.append(df) else: print(f"Error: No data available for {ticker}") if ticker == "^GSPC": # Create a dummy SP500 column with NaN if no data - df = pd.DataFrame(index=pd.date_range(start=start_date, end=end_date, freq='1H')) - df['SP500'] = np.nan + df = pd.DataFrame( + index=pd.date_range( + start=start_date, + end=end_date, + freq="1H" + ) + ) + df["SP500"] = np.nan print(f"Dummy S&P 500 data shape: {df.shape}") combined_data.append(df) @@ -160,9 +230,13 @@ def _fetch_raw_data(self): if combined_data: full_df = combined_data[0] # Silver data for df in combined_data[1:]: - full_df = full_df.join(df, how='left') # Left join to keep silver data + # Left join to keep silver data + full_df = full_df.join(df, how="left") print(f"Combined DataFrame columns: {full_df.columns.tolist()}") - print(f"Combined DataFrame shape: {full_df.shape}, non-NaN SP500 counts: {full_df['SP500'].count() if 'SP500' in full_df.columns else 0}") + print( + f"Combined DataFrame shape: {full_df.shape}, non-NaN \ +SP500 counts: {full_df['SP500'].count() if 'SP500' in full_df.columns else 0}" + ) print(f"Combined DataFrame head:\n{full_df.head()}") full_df.to_csv(self.raw_data_path) else: @@ -172,47 +246,74 @@ def _fetch_raw_data(self): def _calculate_indicators(self, df): """Adds indicators and handles S&P 500""" - expected_columns = ['Close', 'High', 'Low', 'Open', 'Volume', 'SP500'] + expected_columns = ["Close", "High", "Low", "Open", "Volume", "SP500"] for col in expected_columns: if col in df.columns: - df[col] = pd.to_numeric(df[col], errors='coerce') + df[col] = pd.to_numeric(df[col], errors="coerce") else: - print(f"Warning: Column {col} missing in DataFrame. Adding with NaN.") + print(f"Warning: Column {col} missing in \ +DataFrame. Adding with NaN.") df[col] = np.nan - df.dropna(subset=['Close', 'High', 'Low', 'Open'], inplace=True) + df.dropna(subset=["Close", "High", "Low", "Open"], inplace=True) # Interpolate SP500 to handle missing values - if 'SP500' in df.columns: - non_nan_before = df['SP500'].count() - df['SP500'] = df['SP500'].interpolate(method='time', limit_direction='both') - non_nan_after = df['SP500'].count() - print(f"SP500 non-NaN counts before interpolation: {non_nan_before}, after: {non_nan_after}") + if "SP500" in df.columns: + non_nan_before = df["SP500"].count() + df["SP500"] = df["SP500"].interpolate( + method="time", + limit_direction="both" + ) + non_nan_after = df["SP500"].count() + print( + f"SP500 non-NaN counts before interpolation: \ +{non_nan_before}, after: {non_nan_after}" + ) print(f"SP500 sample values:\n{df['SP500'].head()}") if non_nan_after < len(df) * 0.1: - print(f"Warning: SP500 has very few non-NaN values ({non_nan_after}/{len(df)}) after interpolation.") + print( + f"Warning: SP500 has very few non-NaN values \ +({non_nan_after}/{len(df)}) after interpolation." + ) else: - print("Warning: SP500 column missing after processing. Adding with NaN.") - df['SP500'] = np.nan + print("Warning: SP500 column missing after \ +processing. Adding with NaN.") + df["SP500"] = np.nan - df['EMA20'] = ta.trend.EMAIndicator(df['Close'], window=20).ema_indicator() - df['RSI14'] = ta.momentum.RSIIndicator(df['Close'], window=14).rsi() - df['ATR14'] = ta.volatility.AverageTrueRange(df['High'], df['Low'], df['Close'], window=14).average_true_range() + df["EMA20"] = ta.trend.EMAIndicator( + df["Close"], window=20 + ).ema_indicator() + df["RSI14"] = ta.momentum.RSIIndicator(df["Close"], window=14).rsi() + df["ATR14"] = ta.volatility.AverageTrueRange( + df["High"], df["Low"], df["Close"], window=14 + ).average_true_range() - macd = ta.trend.MACD(df['Close'], window_slow=26, window_fast=12, window_sign=9) - df['MACD'] = macd.macd() - df['MACD_Signal'] = macd.macd_signal() - df['MACD_Hist'] = macd.macd_diff() + macd = ta.trend.MACD( + df["Close"], + window_slow=26, + window_fast=12, + window_sign=9 + ) + df["MACD"] = macd.macd() + df["MACD_Signal"] = macd.macd_signal() + df["MACD_Hist"] = macd.macd_diff() print(f"Processed DataFrame columns: {df.columns.tolist()}") - print(f"Processed DataFrame shape: {df.shape}, non-NaN SP500 counts: {df['SP500'].count() if 'SP500' in df.columns else 0}") + print( + f"Processed DataFrame shape: {df.shape}, \ +non-NaN SP500 counts: \ +{df['SP500'].count() if 'SP500' in df.columns else 0}" + ) print(f"Processed DataFrame head:\n{df.head()}") return df def _needs_update(self): """Force refresh to ensure SP500 is included""" - return True # Remove this line and uncomment below after confirming SP500 is included + return True # Remove this line and uncomment below after + # confirming SP500 is included # if not os.path.exists(self.processed_data_path): # return True - # last_modified = datetime.fromtimestamp(os.path.getmtime(self.processed_data_path)) + # last_modified = datetime.fromtimestamp(os.path.getmtime( + # self.processed_data_path + # )) # return (datetime.now() - last_modified) > timedelta(days=1) def load_data(self): @@ -228,12 +329,19 @@ def load_data(self): self.data = processed_df else: print("Previously uploaded silver data is used") - self.data = pd.read_csv(self.processed_data_path, index_col=0, parse_dates=True) + self.data = pd.read_csv( + self.processed_data_path, index_col=0, parse_dates=True + ) self.data = self.data[~self.data.index.duplicated()] - if 'SP500' not in self.data.columns: - print("Warning: SP500 column missing in loaded data. Adding with NaN.") - self.data['SP500'] = np.nan + if "SP500" not in self.data.columns: + print("Warning: SP500 column missing in \ +loaded data. Adding with NaN.") + self.data["SP500"] = np.nan print(f"Final DataFrame columns: {self.data.columns.tolist()}") - print(f"Final DataFrame shape: {self.data.shape}, non-NaN SP500 counts: {self.data['SP500'].count() if 'SP500' in self.data.columns else 0}") + print( + f"Final DataFrame shape: {self.data.shape}, \ +non-NaN SP500 counts: \ +{self.data['SP500'].count() if 'SP500' in self.data.columns else 0}" + ) print(f"Final DataFrame head:\n{self.data.head()}") - return self.data \ No newline at end of file + return self.data diff --git a/data/data_updater.py b/data/data_updater.py index 59ebebc..aa425f3 100644 --- a/data/data_updater.py +++ b/data/data_updater.py @@ -1,11 +1,12 @@ import time -from fetch_gold_ohlcv import fetch_gold_ohlcv -from calculate_indicators import calculate_indicators from insert_forecast_to_db import insert_forecast_to_db from update_model import update_model from parse_metallurgy_news import parse_metallurgy_news +import datetime as dt -hour_counter = 0 +# Take today as yesterday to have +# first data updating +today = dt.date.today() - dt.timedelta(days=1) metal_list = [ 'gold', @@ -13,10 +14,11 @@ ] while True: - if hour_counter == 0: + if today != dt.date.today(): parse_metallurgy_news() update_model() - hour_counter = (hour_counter + 1) % 24 + today = dt.date.today() + for metal_id in metal_list: insert_forecast_to_db(metal_id) time.sleep(3600) diff --git a/data/forecasting_framework.py b/data/forecasting_framework.py index 8b021ef..df4065e 100644 --- a/data/forecasting_framework.py +++ b/data/forecasting_framework.py @@ -1,7 +1,8 @@ import pandas as pd import matplotlib.pyplot as plt from matplotlib.dates import DayLocator, DateFormatter -from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error +from sklearn.metrics import mean_absolute_error, mean_squared_error, \ + mean_absolute_percentage_error import os from forecasting_models import ForecastModel, ClosePriceFM from data_loader import GoldDataLoader @@ -42,7 +43,8 @@ def __init__( self.test_set = None else: train_size = int(len(self.df) * train_size) - self.train_set, self.test_set = self.df.iloc[:train_size], self.df.iloc[train_size:] + self.train_set, self.test_set = self.df.iloc[:train_size], \ + self.df.iloc[train_size:] self.forecast_model = forecast_model self.name = name @@ -64,7 +66,9 @@ def evaluate(self, """ if (self.test_set is None): raise ValueError("No test set were provided") - forecast_values = self.forecast_model.predict(self.test_set.index).to_numpy().reshape(-1) + forecast_values = self.forecast_model.predict( + self.test_set.index + ).to_numpy().reshape(-1) true_values = self.test_set[self.target_columns].to_numpy() results = dict(keys=metric_funcs.keys()) for metric, func in metric_funcs.items(): @@ -77,17 +81,38 @@ def plot_forecast(self): """ if (self.test_set is None): raise ValueError("No test set were provided") - forecast_values = self.forecast_model.predict(self.test_set.index).to_numpy().reshape(-1) + forecast_values = self.forecast_model.predict( + self.test_set.index + ).to_numpy().reshape(-1) true_values = self.test_set[self.target_columns].to_numpy() - fig, ax = plt.subplots(nrows=true_values.shape[1], figsize=(12, 8), squeeze=False) + fig, ax = plt.subplots( + nrows=true_values.shape[1], + figsize=(12, 8), + squeeze=False + ) for target_idx in range(true_values.shape[1]): - ax[target_idx, 0].plot(self.test_set.index, true_values) - ax[target_idx, 0].plot(self.test_set.index, forecast_values, linestyle='--') - ax[target_idx, 0].xaxis.set_major_locator(DayLocator(interval=1)) - ax[target_idx, 0].xaxis.set_major_formatter(DateFormatter('%b, %d')) - ax[target_idx, 0].set_ylabel(self.target_columns[target_idx]) + ax[ + target_idx, + 0 + ].plot(self.test_set.index, true_values) + ax[ + target_idx, + 0 + ].plot(self.test_set.index, forecast_values, linestyle='--') + ax[ + target_idx, + 0 + ].xaxis.set_major_locator(DayLocator(interval=1)) + ax[ + target_idx, + 0 + ].xaxis.set_major_formatter(DateFormatter('%b, %d')) + ax[ + target_idx, + 0 + ].set_ylabel(self.target_columns[target_idx]) return fig def dump_model(self, path: str = None) -> None: @@ -127,7 +152,13 @@ def load_from_file( ForecastFramework: constructed framework object. """ assert os.path.exists(path) - framework = ForecastFramework(data_loader, target_columns, forecast_model, name, train_size) + framework = ForecastFramework( + data_loader, + target_columns, + forecast_model, + name, + train_size + ) framework.forecast_model.load(framework.train_set, path) return framework @@ -160,6 +191,9 @@ def forecast_interval_(self, value: int, unit: str) -> pd.DatetimeIndex: date_index = self.df.index return pd.date_range( date_index[-1] + pd.Timedelta(value=30, unit='min'), - date_index[-1] + pd.Timedelta(value=30, unit='min') + pd.Timedelta(value=value, unit=unit), + date_index[-1] + pd.Timedelta( + value=30, + unit='min' + ) + pd.Timedelta(value=value, unit=unit), freq='30min' ) diff --git a/data/forecasting_models.py b/data/forecasting_models.py index d3e940d..e4ba2e2 100644 --- a/data/forecasting_models.py +++ b/data/forecasting_models.py @@ -17,6 +17,7 @@ from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.saving import load_model import json +import os class ForecastModel(ABC): @@ -27,7 +28,8 @@ class ForecastModel(ABC): @abstractmethod def fit(self, df: pd.DataFrame, params: Dict[str, Any]): """ - Creates dataset from `df` and fits the model with parameters to the dataset. + Creates dataset from `df` and fits the model with parameters to + the dataset. Parameters: df (pd.DataFrame): dataframe from which dataset is built. @@ -65,14 +67,17 @@ def build_forecast_data(self, df: pd.DataFrame) -> pd.DataFrame: pass @abstractmethod - def cross_validation(self, - df: pd.DataFrame, - params: Dict[str, Any], - K: int = 20, - test_size: int = 24*10*1, - metric: Callable = mean_squared_error) -> float: + def cross_validation( + self, + df: pd.DataFrame, + params: Dict[str, Any], + K: int = 20, + test_size: int = 24 * 10 * 1, + metric: Callable = mean_squared_error, + ) -> float: """ - K-fold cross validation of a given test size to evaluate model performance. + K-fold cross validation of a given test size to evaluate model + performance. Parameters: df (pd.DataFrame): dataframe. @@ -148,11 +153,14 @@ def build(self, df: pd.DataFrame) -> pd.DataFrame: def build_forecast_data(self, df: pd.DataFrame) -> pd.DataFrame: return self.build(df) - def cross_validation(self, - df: pd.DataFrame, - params: Dict[str, Any], - K: int = 20, test_size: int = 24*10*1, - metric: Callable = mean_squared_error) -> float: + def cross_validation( + self, + df: pd.DataFrame, + params: Dict[str, Any], + K: int = 20, + test_size: int = 24 * 10 * 1, + metric: Callable = mean_squared_error, + ) -> float: return None def fit(self, df: pd.DataFrame): @@ -163,23 +171,31 @@ def fit(self, df: pd.DataFrame): def predict(self, date_range: pd.DatetimeIndex) -> pd.Series: assert self.model_fit is not None - y_pred = self.model_fit.predict(start=date_range[0], end=date_range[-1], dynamic=False) + y_pred = self.model_fit.predict( + start=date_range[0], end=date_range[-1], dynamic=False + ) return y_pred def dump(self, path: str) -> None: self.model_fit.save(f"{path}/{self.feature_name}_predictor.joblib") def load(self, df: pd.DataFrame, path: str) -> None: - self.model_fit = ARIMAResults.load(f"{path}/{self.feature_name}_predictor.joblib") + self.model_fit = ARIMAResults.load( + f"{path}/{self.feature_name}_predictor.joblib" + ) -def decompose_tabular_data(data: np.array, h: int) -> Tuple[np.array, np.array]: +def decompose_tabular_data(data: np.array, h: int) -> Tuple[ + np.array, + np.array +]: """ Compose features from successive dataset objects using sliding window. For example, [y_1, y_2, y_3, y_4, ...] (h=2) would produce - ([ [*y_1, *y_2, y_3[:, 1:]], [*y_2, *y_3, y_4[:, 1:]], ...], [y_3, y_4, ...]). + ([ [*y_1, *y_2, y_3[:, 1:]], [*y_2, *y_3, y_4[:, 1:]], ...], + [y_3, y_4, ...]). Here *a means unpacking values from a vector. @@ -191,18 +207,24 @@ def decompose_tabular_data(data: np.array, h: int) -> Tuple[np.array, np.array]: Tuple[np.array, np.array]: tuple of sampled dataset of h features and the target value for them. """ - X = sliding_window_view(data, window_shape=(h, data.shape[1])).reshape(-1, h * data.shape[1])[:-1, :] + X = sliding_window_view(data, window_shape=(h, data.shape[1])).reshape( + -1, h * data.shape[1] + )[:-1, :] y = data[h:] target, features = y[:, 0], y[:, 1:] X = np.hstack([X, features, np.ones(shape=(X.shape[0], 1))]) return (X, target) -def compose_forecast_frame(data: np.array, features: np.array, lag: int) -> Tuple[np.array, np.array]: +def compose_forecast_frame( + data: np.array, features: np.array, lag: int +) -> Tuple[np.array, np.array]: """ - Compose features from the last observations and features of forecast timeframe. + Compose features from the last observations and features of + forecast timeframe. - For example, [..., y_{n-k}, ..., y_{n-3}, y_{n-2}, y_{n-1}, y_n] (lag=2) would produce + For example, [..., y_{n-k}, ..., y_{n-3}, y_{n-2}, y_{n-1}, y_n] + (lag=2) would produce [*y_{n-1}, *y_{n}, *features, 1]. @@ -210,14 +232,19 @@ def compose_forecast_frame(data: np.array, features: np.array, lag: int) -> Tupl Parameters: data (np.array): 2D-array of target observations. - features (np.array): 1D-array of current forecast timeframe features. + features (np.array): 1D-array of current forecast + timeframe features. lag (int): number of lagged observations to include. Returns: 1D-array of features for the model prediction. """ X = data[-lag:].reshape(-1, lag * data.shape[1]) - X = np.hstack([X, features.reshape(1, -1), np.ones(shape=(X.shape[0], 1))]) + X = np.hstack([ + X, + features.reshape(1, -1), + np.ones(shape=(X.shape[0], 1)) + ]) return X @@ -229,9 +256,11 @@ class XGBoostFM(ForecastModel): model_ (XGBRegressor): regression model. df_ (pd.DataFrame): dataset built for training. target (str): target column. - stationary (bool): True if series is stationary (first difference is irrelevant). + stationary (bool): True if series is stationary + (first difference is irrelevant). lag (int): number of lagged features. - last_value_ (float): last observed value of the series (for differenced predictions). + last_value_ (float): last observed value of the series + (for differenced predictions). """ model_: BaseEstimator @@ -266,7 +295,7 @@ def preprocess_(self, df: pd.DataFrame) -> pd.DataFrame: outliers = (df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR)) df.loc[outliers] = np.nan - df = df.interpolate(method='time') + df = df.interpolate(method="time") return df @@ -277,50 +306,65 @@ def build(self, df: pd.DataFrame) -> pd.DataFrame: df = pd.DataFrame(self.preprocess_(df[self.target])) # Obtain first difference (if relevant) - if (not self.stationary): + if not self.stationary: self.last_value_ = df.iloc[-1][self.target] df = df.diff().dropna() # Day of Week (0=Monday, 6=Sunday) - df['day_of_week'] = df.index.dayofweek - df['day_of_week'] = pd.Categorical(df['day_of_week'], categories=range(7), ordered=True) + df["day_of_week"] = df.index.dayofweek + df["day_of_week"] = pd.Categorical( + df["day_of_week"], categories=range(7), ordered=True + ) - df['year'] = df.index.year - df['year'] = pd.Categorical(df['year'], categories=range(2023, 2026), ordered=True) + df["year"] = df.index.year + df["year"] = pd.Categorical( + df["year"], categories=range(2023, 2026), ordered=True + ) # Month (1-12) month_index = df.index.month # Cyclical encoding for months (12-month period) - df['month_sin'] = np.sin(2 * np.pi * month_index / 12) - df['month_cos'] = np.cos(2 * np.pi * month_index / 12) + df["month_sin"] = np.sin(2 * np.pi * month_index / 12) + df["month_cos"] = np.cos(2 * np.pi * month_index / 12) # Season (1=Winter, 2=Spring, 3=Summer, 4=Fall) - df['season'] = (df.index.month % 12 + 3) // 3 - df['season'] = pd.Categorical(df['season'], categories=range(1, 5), ordered=True) + df["season"] = (df.index.month % 12 + 3) // 3 + df["season"] = pd.Categorical( + df["season"], categories=range(1, 5), ordered=True + ) # Weekend flag (1 if Saturday/Sunday, else 0) - df['is_weekend'] = df.index.dayofweek.isin([5, 6]).astype(int) + df["is_weekend"] = df.index.dayofweek.isin([5, 6]).astype(int) # Hour hour_index = df.index.hour # Cyclical encoding for hours (24h period) - df['hour_sin'] = np.sin(2 * np.pi * hour_index / 24) - df['hour_cos'] = np.cos(2 * np.pi * hour_index / 24) + df["hour_sin"] = np.sin(2 * np.pi * hour_index / 24) + df["hour_cos"] = np.cos(2 * np.pi * hour_index / 24) # For cyclical features (day_of_week, season) - df = pd.get_dummies(df, columns=['day_of_week', 'season'], prefix=['dow', 'season']) + df = pd.get_dummies( + df, columns=["day_of_week", "season"], prefix=["dow", "season"] + ) # Normalization - for feature in ['year', 'month_sin', 'month_cos', 'hour_sin', 'hour_cos']: + for feature in [ + "year", + "month_sin", + "month_cos", + "hour_sin", + "hour_cos" + ]: df[feature] = MinMaxScaler().fit_transform(df[[feature]]) return df.astype(np.float32) def build_forecast_data(self, df: pd.DataFrame): """ - Builds forecasting dataset (indexed with forecasting date range) from the dataframe. + Builds forecasting dataset (indexed with forecasting date range) + from the dataframe. Parameters: df (pd.DataFrame): dataframe from which dataset is built. @@ -334,53 +378,72 @@ def build_forecast_data(self, df: pd.DataFrame): df = pd.DataFrame(df[self.target]) # Day of Week (0=Monday, 6=Sunday) - df['day_of_week'] = df.index.dayofweek - df['day_of_week'] = pd.Categorical(df['day_of_week'], categories=range(7), ordered=True) + df["day_of_week"] = df.index.dayofweek + df["day_of_week"] = pd.Categorical( + df["day_of_week"], categories=range(7), ordered=True + ) - df['year'] = df.index.year - df['year'] = pd.Categorical(df['year'], categories=range(2023, 2026), ordered=True) + df["year"] = df.index.year + df["year"] = pd.Categorical( + df["year"], categories=range(2023, 2026), ordered=True + ) # Month (1-12) month_index = df.index.month # Cyclical encoding for months (12-month period) - df['month_sin'] = np.sin(2 * np.pi * month_index / 12) - df['month_cos'] = np.cos(2 * np.pi * month_index / 12) + df["month_sin"] = np.sin(2 * np.pi * month_index / 12) + df["month_cos"] = np.cos(2 * np.pi * month_index / 12) # Season (1=Winter, 2=Spring, 3=Summer, 4=Fall) - df['season'] = (df.index.month % 12 + 3) // 3 - df['season'] = pd.Categorical(df['season'], categories=range(1, 5), ordered=True) + df["season"] = (df.index.month % 12 + 3) // 3 + df["season"] = pd.Categorical( + df["season"], categories=range(1, 5), ordered=True + ) # Weekend flag (1 if Saturday/Sunday, else 0) - df['is_weekend'] = df.index.dayofweek.isin([5, 6]).astype(int) + df["is_weekend"] = df.index.dayofweek.isin([5, 6]).astype(int) # Hour hour_index = df.index.hour # Cyclical encoding for hours (24h period) - df['hour_sin'] = np.sin(2 * np.pi * hour_index / 24) - df['hour_cos'] = np.cos(2 * np.pi * hour_index / 24) + df["hour_sin"] = np.sin(2 * np.pi * hour_index / 24) + df["hour_cos"] = np.cos(2 * np.pi * hour_index / 24) # For cyclical features (day_of_week, season) - df = pd.get_dummies(df, columns=['day_of_week', 'season'], prefix=['dow', 'season']) + df = pd.get_dummies( + df, columns=["day_of_week", "season"], prefix=["dow", "season"] + ) # Normalization - for feature in ['year', 'month_sin', 'month_cos', 'hour_sin', 'hour_cos']: + for feature in [ + "year", + "month_sin", + "month_cos", + "hour_sin", + "hour_cos" + ]: df[feature] = MinMaxScaler().fit_transform(df[[feature]]) return df.astype(np.float32) - def cross_validation(self, - df: pd.DataFrame, - params: Dict[str, Any], - K: int = 20, - test_size: int = 24*10*1, - metric: Callable = mean_squared_error) -> float: + def cross_validation( + self, + df: pd.DataFrame, + params: Dict[str, Any], + K: int = 20, + test_size: int = 24 * 10 * 1, + metric: Callable = mean_squared_error, + ) -> float: history_df = self.build(df) tss = TimeSeriesSplit(n_splits=K, test_size=test_size, gap=24) - X_train, y_train = decompose_tabular_data(history_df.to_numpy(), h=self.lag) + X_train, y_train = decompose_tabular_data( + history_df.to_numpy(), + h=self.lag + ) Xy = pd.DataFrame(X_train, y_train) score = 0 @@ -393,21 +456,26 @@ def cross_validation(self, X_val, y_val = val.to_numpy(), val.index.to_numpy() regressor = XGBRegressor( - n_estimators=params['n_estimators'], - learning_rate=params['learning_rate'], - max_depth=params['max_depth'], - min_child_weight=params['min_child_weight'], - subsample=params['subsample'], - colsample_bytree=params['colsample_bytree'], - gamma=params['gamma'], - scale_pos_weight=np.sqrt(len(y_train)/y_train.sum()), - tree_method='hist', - booster='gbtree', - objective='reg:squarederror', - random_state=42 + n_estimators=params["n_estimators"], + learning_rate=params["learning_rate"], + max_depth=params["max_depth"], + min_child_weight=params["min_child_weight"], + subsample=params["subsample"], + colsample_bytree=params["colsample_bytree"], + gamma=params["gamma"], + scale_pos_weight=np.sqrt(len(y_train) / y_train.sum()), + tree_method="hist", + booster="gbtree", + objective="reg:squarederror", + random_state=42, ) - regressor.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=False) + regressor.fit( + X_train, + y_train, + eval_set=[(X_train, y_train), (X_val, y_val)], + verbose=False, + ) y_pred = regressor.predict(X_val) @@ -425,55 +493,76 @@ def fit( "min_child_weight": 8.7, "subsample": 1, "colsample_bytree": 1, - "gamma": 3 - } + "gamma": 3, + }, ): self.df_ = self.build(df) train_size = int(len(self.df_) * 0.8) - train_set, test_set = self.df_.iloc[:train_size], self.df_.iloc[train_size:] + train_set, test_set = self.df_.iloc[:train_size], \ + self.df_.iloc[train_size:] - X_train, y_train = decompose_tabular_data(train_set.to_numpy(), h=self.lag) - X_test, y_test = decompose_tabular_data(test_set.to_numpy(), h=self.lag) + X_train, y_train = decompose_tabular_data( + train_set.to_numpy(), + h=self.lag + ) + X_test, y_test = decompose_tabular_data( + test_set.to_numpy(), + h=self.lag + ) self.model_ = XGBRegressor( - n_estimators=params['n_estimators'], - learning_rate=params['learning_rate'], - max_depth=params['max_depth'], - min_child_weight=params['min_child_weight'], - subsample=params['subsample'], - colsample_bytree=params['colsample_bytree'], - gamma=params['gamma'], - scale_pos_weight=np.sqrt(len(y_train)/y_train.sum()), - tree_method='hist', + n_estimators=params["n_estimators"], + learning_rate=params["learning_rate"], + max_depth=params["max_depth"], + min_child_weight=params["min_child_weight"], + subsample=params["subsample"], + colsample_bytree=params["colsample_bytree"], + gamma=params["gamma"], + scale_pos_weight=np.sqrt(len(y_train) / y_train.sum()), + tree_method="hist", booster=None, - objective='reg:squarederror', - random_state=42 + objective="reg:squarederror", + random_state=42, ) - self.model_.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=False) + self.model_.fit( + X_train, + y_train, + eval_set=[(X_train, y_train), (X_test, y_test)], + verbose=False, + ) return self def predict(self, date_range: pd.DatetimeIndex) -> pd.Series: - prediction_df = self.build_forecast_data(pd.DataFrame(index=date_range, columns=[self.target])) + prediction_df = self.build_forecast_data( + pd.DataFrame(index=date_range, columns=[self.target]) + ) history_df = self.df_.copy() for date in date_range: - feature_columns = [column for column in prediction_df.columns if column != self.target] + feature_columns = [ + column + for column + in prediction_df.columns + if column != self.target + ] features = prediction_df.loc[date, feature_columns].copy() - x_ = compose_forecast_frame(history_df.to_numpy(), features.to_numpy(), self.lag) + x_ = compose_forecast_frame( + history_df.to_numpy(), features.to_numpy(), self.lag + ) y_ = self.model_.predict(x_) features.loc[self.target] = y_ history_df = pd.concat([history_df, features.to_frame().T]) - y_pred = history_df.loc[date_range[0]:date_range[-1], self.target] + y_pred = history_df.loc[date_range[0]: date_range[-1], self.target] - if (self.stationary): + if self.stationary: return y_pred return self.last_value_ + np.cumsum(y_pred) @@ -494,7 +583,8 @@ class ClosePriceFM(ForecastModel): Attributes: model_ (BaseEstimator): regression model. - feature_models (Dict[str, ForecastModel]): dictionary of auxiliary models and their names. + feature_models (Dict[str, ForecastModel]): dictionary of auxiliary + models and their names. df_ (pd.DataFrame): dataset built for training. last_close_price (float): price of the last observed close prices. lag (int): number of lagged features. @@ -507,17 +597,24 @@ class ClosePriceFM(ForecastModel): last_close_price: float lag: int train_size: float - indicators: List[str] = ['EMA20', 'RSI14', 'ATR14', 'MACD', 'MACD_Signal', 'MACD_Hist'] + indicators: List[str] = [ + "EMA20", + "RSI14", + "ATR14", + "MACD", + "MACD_Signal", + "MACD_Hist", + ] def __init__( - self, - lag: int = 50, - train_size: float = 0.7, - feature_models: Dict[str, ForecastModel] = { - 'High': XGBoostFM('High', stationary=False), - 'Low': XGBoostFM('Low', stationary=False), - 'Volume': XGBoostFM('Volume') - } + self, + lag: int = 50, + train_size: float = 0.7, + feature_models: Dict[str, ForecastModel] = { + "High": XGBoostFM("High", stationary=False), + "Low": XGBoostFM("Low", stationary=False), + "Volume": XGBoostFM("Volume"), + }, ): self.lag = lag self.train_size = train_size @@ -528,15 +625,16 @@ def build(self, df: pd.DataFrame): df = df.copy() # First difference to remove trend - self.last_close_price = df['Close'].iloc[-1] - df.loc[:, 'Close'] = df['Close'].diff() + self.last_close_price = df["Close"].iloc[-1] + df.loc[:, "Close"] = df["Close"].diff() df = df.dropna() return df def build_forecast_data(self, df: pd.DataFrame) -> pd.DataFrame: """ - Builds forecasting dataset (indexed with forecasting date range) from the dataframe. + Builds forecasting dataset (indexed with forecasting date range) + from the dataframe. Parameters: df (pd.DataFrame): dataframe from which dataset is built. @@ -551,11 +649,11 @@ def build_forecast_data(self, df: pd.DataFrame) -> pd.DataFrame: for feature, feature_model in self.feature_models.items(): df.loc[:, feature] = feature_model.predict(df.index) # Set `Open` price - df.loc[:, 'Open'] = np.nan - df.loc[df.index[0], 'Open'] = self.df_['Close'].iloc[-1] + df.loc[:, "Open"] = np.nan + df.loc[df.index[0], "Open"] = self.df_["Close"].iloc[-1] # Set `Close` price - df.loc[:, 'Close'] = np.nan + df.loc[:, "Close"] = np.nan # Set Indicators for indicator in self.indicators: @@ -564,17 +662,22 @@ def build_forecast_data(self, df: pd.DataFrame) -> pd.DataFrame: return df - def cross_validation(self, - df: pd.DataFrame, - params: Dict[str, Any], - K: int = 20, - test_size: int = 24*10*1, - metric: Callable = mean_squared_error) -> float: + def cross_validation( + self, + df: pd.DataFrame, + params: Dict[str, Any], + K: int = 20, + test_size: int = 24 * 10 * 1, + metric: Callable = mean_squared_error, + ) -> float: history_df = self.build(df) tss = TimeSeriesSplit(n_splits=K, test_size=test_size, gap=24) - X_train, y_train = decompose_tabular_data(history_df.to_numpy(), h=self.lag) + X_train, y_train = decompose_tabular_data( + history_df.to_numpy(), + h=self.lag + ) Xy = pd.DataFrame(X_train, y_train) score = 0 @@ -587,20 +690,25 @@ def cross_validation(self, X_val, y_val = val.to_numpy(), val.index.to_numpy() regressor = XGBRegressor( - n_estimators=params['n_estimators'], - learning_rate=params['learning_rate'], - max_depth=params['max_depth'], - min_child_weight=params['min_child_weight'], - subsample=params['subsample'], - colsample_bytree=params['colsample_bytree'], - gamma=params['gamma'], - tree_method='hist', + n_estimators=params["n_estimators"], + learning_rate=params["learning_rate"], + max_depth=params["max_depth"], + min_child_weight=params["min_child_weight"], + subsample=params["subsample"], + colsample_bytree=params["colsample_bytree"], + gamma=params["gamma"], + tree_method="hist", booster=None, - objective='reg:squarederror', - random_state=42 + objective="reg:squarederror", + random_state=42, ) - regressor.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=False) + regressor.fit( + X_train, + y_train, + eval_set=[(X_train, y_train), (X_val, y_val)], + verbose=False, + ) y_pred = regressor.predict(X_val) @@ -618,90 +726,168 @@ def fit( "min_child_weight": 4, "subsample": 0.7, "colsample_bytree": 1.0, - "gamma": 2 - } + "gamma": 2, + }, ): self.df_ = self.build(df) # Fit feature models - for feature_model in self.feature_models.values(): + print("Fitting feature models...") + for key, feature_model in self.feature_models.items(): + print(f" -> Fitting model for {key}") feature_model.fit(df) + print("Feature models fitted.") train_size = int(len(self.df_) * self.train_size) - train_set, test_set = self.df_.iloc[:train_size], self.df_.iloc[train_size:] + train_set, test_set = self.df_.iloc[:train_size], \ + self.df_.iloc[train_size:] - X_train, y_train = decompose_tabular_data(train_set.to_numpy(), h=self.lag) - X_test, y_test = decompose_tabular_data(test_set.to_numpy(), h=self.lag) + X_train, y_train = decompose_tabular_data( + train_set.to_numpy(), + h=self.lag + ) + X_test, y_test = decompose_tabular_data( + test_set.to_numpy(), + h=self.lag + ) self.model_ = XGBRegressor( - n_estimators=params['n_estimators'], - learning_rate=params['learning_rate'], - max_depth=params['max_depth'], - min_child_weight=params['min_child_weight'], - subsample=params['subsample'], - colsample_bytree=params['colsample_bytree'], - gamma=params['gamma'], - scale_pos_weight=np.sqrt(len(y_train)/y_train.sum()), - tree_method='hist', + n_estimators=params["n_estimators"], + learning_rate=params["learning_rate"], + max_depth=params["max_depth"], + min_child_weight=params["min_child_weight"], + subsample=params["subsample"], + colsample_bytree=params["colsample_bytree"], + gamma=params["gamma"], + scale_pos_weight=np.sqrt(len(y_train) / y_train.sum()), + tree_method="hist", booster=None, - objective='reg:squarederror', + objective="reg:squarederror", random_state=42, ) - self.model_.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=False) + print("Fitting Close model...") + self.model_.fit( + X_train, + y_train, + eval_set=[(X_train, y_train), (X_test, y_test)], + verbose=False, + ) + print("Close model fitted.") return self def predict(self, date_range: pd.DatetimeIndex) -> pd.Series: - prediction_df = self.build_forecast_data(pd.DataFrame(index=date_range)) + prediction_df = self.build_forecast_data( + pd.DataFrame(index=date_range) + ) history_df = self.df_.copy() for idx, date in enumerate(date_range): - feature_columns = [column for column in prediction_df.columns if column != 'Close'] + feature_columns = [ + column + for column + in prediction_df.columns + if column != "Close" + ] features = prediction_df.loc[date, feature_columns].copy() - x_ = compose_forecast_frame(history_df.to_numpy(), features.to_numpy(), self.lag) + x_ = compose_forecast_frame( + history_df.to_numpy(), features.to_numpy(), self.lag + ) y_ = self.model_.predict(x_) - history_close_price = pd.concat([history_df['Close'], pd.Series(y_, index=[date])]) - history_high_price = pd.concat([history_df['High'], pd.Series(features['High'], index=[date])]) - history_low_price = pd.concat([history_df['Low'], pd.Series(features['Low'], index=[date])]) + history_close_price = pd.concat( + [history_df["Close"], pd.Series(y_, index=[date])] + ) + history_high_price = pd.concat( + [ + history_df["High"], + pd.Series(features["High"], index=[date]) + ] + ) + history_low_price = pd.concat( + [history_df["Low"], pd.Series(features["Low"], index=[date])] + ) # Indicators - features.loc['EMA20'] = ta.trend.EMAIndicator(history_close_price, window=20).ema_indicator().iloc[-1] - features.loc['RSI14'] = ta.momentum.RSIIndicator(history_close_price, window=14).rsi().iloc[-1] - features.loc['ATR14'] = ta.volatility.AverageTrueRange( - history_high_price, history_low_price, history_close_price, window=14 - ).average_true_range().iloc[-1] + features.loc["EMA20"] = ( + ta.trend.EMAIndicator(history_close_price, window=20) + .ema_indicator() + .iloc[-1] + ) + features.loc["RSI14"] = ( + ta.momentum.RSIIndicator( + history_close_price, + window=14 + ).rsi().iloc[-1] + ) + features.loc["ATR14"] = ( + ta.volatility.AverageTrueRange( + history_high_price, + history_low_price, + history_close_price, + window=14, + ) + .average_true_range() + .iloc[-1] + ) - macd = ta.trend.MACD(history_close_price, window_slow=26, window_fast=12, window_sign=9) - features.loc['MACD'] = macd.macd().iloc[-1] - features.loc['MACD_Signal'] = macd.macd_signal().iloc[-1] - features.loc['MACD_Hist'] = macd.macd_diff().iloc[-1] + macd = ta.trend.MACD( + history_close_price, + window_slow=26, + window_fast=12, + window_sign=9 + ) + features.loc["MACD"] = macd.macd().iloc[-1] + features.loc["MACD_Signal"] = macd.macd_signal().iloc[-1] + features.loc["MACD_Hist"] = macd.macd_diff().iloc[-1] # Set future indicator values as current - if idx < len(date_range)-1: + if idx < len(date_range) - 1: for indicator in self.indicators: - prediction_df.loc[date_range[idx+1], indicator] = features[indicator] + prediction_df.loc[ + date_range[idx + 1], + indicator + ] = features[ + indicator + ] # Set `Open` price - prediction_df.loc[date_range[idx+1], 'Open'] = history_df['Close'].iloc[-1] - - features.loc['Close'] = y_ - history_df = pd.concat([history_df, pd.DataFrame.from_records([features], index=[date])]) + prediction_df.loc[date_range[idx + 1], "Open"] = history_df[ + "Close" + ].iloc[-1] + + features.loc["Close"] = y_ + history_df = pd.concat( + [ + history_df, + pd.DataFrame.from_records([features], index=[date]) + ] + ) # Recover original time series - y_pred = history_df.loc[date_range[0]:date_range[-1], 'Close'] + y_pred = history_df.loc[date_range[0]: date_range[-1], "Close"] price_prediction = self.last_close_price + np.cumsum(y_pred) return price_prediction def dump(self, path: str) -> None: - joblib.dump(self.model_, f"{path}/Close_predictor.joblib") - for model in self.feature_models.values(): - model.dump(path) + os.makedirs(path, exist_ok=True) + + if hasattr(self, "model_") and self.model_ is not None: + joblib.dump(self.model_, f"{path}/Close_predictor.joblib") + print(f"Saved Close model to {path}/Close_predictor.joblib") + else: + print("Warning: Close model not found, skipping save.") + + for key, model in self.feature_models.items(): + if model is not None: + model.dump(path) + else: + print(f"Warning: Feature model {key} is None") def load(self, df: pd.DataFrame, path: str): self.df_ = self.build(df.copy()) @@ -718,30 +904,49 @@ def __init__(self, train_size=0.7): self.last_open_price: float = None self.last_high_price: float = None self.last_low_price: float = None - self.target_names = [ - 'Close', 'High', 'Low', 'Volume' - ] + self.target_names = ["Close", "High", "Low", "Volume"] self.indicators = [ - 'EMA20', 'RSI14', 'ATR14', 'MACD', 'MACD_Signal', 'MACD_Hist' - ] - self.feature_names = self.target_names + self.indicators + [ - 'year', 'month_sin', 'month_cos', 'hour_sin', - 'hour_cos', 'dow_sin', 'dow_cos', 'season_sin', 'season_cos' + "EMA20", + "RSI14", + "ATR14", + "MACD", + "MACD_Signal", + "MACD_Hist", ] + self.feature_names = ( + self.target_names + + self.indicators + + [ + "year", + "month_sin", + "month_cos", + "hour_sin", + "hour_cos", + "dow_sin", + "dow_cos", + "season_sin", + "season_cos", + ] + ) self.epochs = 50 self.patience = 10 def _build_model(self, params: Dict[str, Any]): - model = Sequential([ - Input(shape=(len(self.feature_names), params['lag'])), - LSTM(params['layer1'], return_sequences=True), - Dropout(params['dropout']), - LSTM(params['layer2']), - Dropout(params['dropout']), - Dense(params['layer3'], activation='relu'), - Dense(len(self.target_names)) - ]) - model.compile(optimizer=Adam(learning_rate=params['learning_rate']), loss='mse') + model = Sequential( + [ + Input(shape=(len(self.feature_names), params["lag"])), + LSTM(params["layer1"], return_sequences=True), + Dropout(params["dropout"]), + LSTM(params["layer2"]), + Dropout(params["dropout"]), + Dense(params["layer3"], activation="relu"), + Dense(len(self.target_names)), + ] + ) + model.compile(optimizer=Adam( + learning_rate=params["learning_rate"]), + loss="mse" + ) return model def build(self, df: pd.DataFrame): @@ -749,43 +954,45 @@ def build(self, df: pd.DataFrame): df = df.copy() # First difference to remove trend - self.last_close_price = df['Close'].iloc[-1] - self.last_open_price = df['Open'].iloc[-1] - self.last_high_price = df['High'].iloc[-1] - self.last_low_price = df['Low'].iloc[-1] + self.last_close_price = df["Close"].iloc[-1] + self.last_open_price = df["Open"].iloc[-1] + self.last_high_price = df["High"].iloc[-1] + self.last_low_price = df["Low"].iloc[-1] # First difference for target variables for col in self.target_names: df[col] = df[col].diff() df = df.dropna() - df['year'] = df.index.year - df['year'] = pd.Categorical(df['year'], categories=range(2023, 2026), ordered=True) + df["year"] = df.index.year + df["year"] = pd.Categorical( + df["year"], categories=range(2023, 2026), ordered=True + ) # Month (1-12) month_index = df.index.month # Cyclical encoding for months (12-month period) - df['month_sin'] = np.sin(2 * np.pi * month_index / 12) - df['month_cos'] = np.cos(2 * np.pi * month_index / 12) + df["month_sin"] = np.sin(2 * np.pi * month_index / 12) + df["month_cos"] = np.cos(2 * np.pi * month_index / 12) # Season (1=Winter, 2=Spring, 3=Summer, 4=Fall) season_index = (df.index.month % 12 + 3) // 3 # Season (1-4) → cyclical - df['season_sin'] = np.sin(2 * np.pi * season_index / 4) - df['season_cos'] = np.cos(2 * np.pi * season_index / 4) + df["season_sin"] = np.sin(2 * np.pi * season_index / 4) + df["season_cos"] = np.cos(2 * np.pi * season_index / 4) # Day of week (0-6) → cyclical - df['dow_sin'] = np.sin(2 * np.pi * df.index.dayofweek / 7) - df['dow_cos'] = np.cos(2 * np.pi * df.index.dayofweek / 7) + df["dow_sin"] = np.sin(2 * np.pi * df.index.dayofweek / 7) + df["dow_cos"] = np.cos(2 * np.pi * df.index.dayofweek / 7) # Hour hour_index = df.index.hour # Cyclical encoding for hours (24h period) - df['hour_sin'] = np.sin(2 * np.pi * hour_index / 24) - df['hour_cos'] = np.cos(2 * np.pi * hour_index / 24) + df["hour_sin"] = np.sin(2 * np.pi * hour_index / 24) + df["hour_cos"] = np.cos(2 * np.pi * hour_index / 24) df = df.astype(np.float32) @@ -795,37 +1002,39 @@ def build_forecast_data(self, df: pd.DataFrame) -> pd.DataFrame: df = df.copy() - df['year'] = df.index.year - df['year'] = pd.Categorical(df['year'], categories=range(2023, 2026), ordered=True) + df["year"] = df.index.year + df["year"] = pd.Categorical( + df["year"], categories=range(2023, 2026), ordered=True + ) # Month (1-12) month_index = df.index.month # Cyclical encoding for months (12-month period) - df['month_sin'] = np.sin(2 * np.pi * month_index / 12) - df['month_cos'] = np.cos(2 * np.pi * month_index / 12) + df["month_sin"] = np.sin(2 * np.pi * month_index / 12) + df["month_cos"] = np.cos(2 * np.pi * month_index / 12) # Season (1=Winter, 2=Spring, 3=Summer, 4=Fall) season_index = (df.index.month % 12 + 3) // 3 # Season (1-4) → cyclical - df['season_sin'] = np.sin(2 * np.pi * season_index / 4) - df['season_cos'] = np.cos(2 * np.pi * season_index / 4) + df["season_sin"] = np.sin(2 * np.pi * season_index / 4) + df["season_cos"] = np.cos(2 * np.pi * season_index / 4) # Day of week (0-6) → cyclical - df['dow_sin'] = np.sin(2 * np.pi * df.index.dayofweek / 7) - df['dow_cos'] = np.cos(2 * np.pi * df.index.dayofweek / 7) + df["dow_sin"] = np.sin(2 * np.pi * df.index.dayofweek / 7) + df["dow_cos"] = np.cos(2 * np.pi * df.index.dayofweek / 7) # Hour hour_index = df.index.hour # Cyclical encoding for hours (24h period) - df['hour_sin'] = np.sin(2 * np.pi * hour_index / 24) - df['hour_cos'] = np.cos(2 * np.pi * hour_index / 24) + df["hour_sin"] = np.sin(2 * np.pi * hour_index / 24) + df["hour_cos"] = np.cos(2 * np.pi * hour_index / 24) # Set `Open` price - df.loc[:, 'Open'] = np.nan - df.loc[df.index[0], 'Open'] = self.df_['Close'].iloc[-1] + df.loc[:, "Open"] = np.nan + df.loc[df.index[0], "Open"] = self.df_["Close"].iloc[-1] # Set Indicators for indicator in self.indicators: @@ -851,33 +1060,46 @@ def _prepare_data(self, df: pd.DataFrame, lag: int): return X, y - def cross_validation(self, - df: pd.DataFrame, - params: Dict[str, Any], - K: int = 20, test_size: int = 24*10*1, - metric: Callable = mean_squared_error) -> float: + def cross_validation( + self, + df: pd.DataFrame, + params: Dict[str, Any], + K: int = 20, + test_size: int = 24 * 10 * 1, + metric: Callable = mean_squared_error, + ) -> float: history_df = self.build(df).copy() tss = TimeSeriesSplit(n_splits=K, test_size=test_size, gap=24) - X, y = self._prepare_data(history_df, lag=params['lag']) + X, y = self._prepare_data(history_df, lag=params["lag"]) score = 0 for train_idx, val_idx in tss.split(X): - X_train, X_val = X[train_idx][:-params['lag']], X[val_idx][:-params['lag']] - y_train, y_val = y[train_idx][params['lag']:], y[val_idx][params['lag']:] + X_train, X_val = ( + X[train_idx][: -params["lag"]], + X[val_idx][: -params["lag"]], + ) + y_train, y_val = y[train_idx][params["lag"]:], \ + y[val_idx][params["lag"]:] self.model = self._build_model(params) self.model.fit( - X_train, y_train, + X_train, + y_train, validation_data=(X_val, y_val), epochs=self.epochs, batch_size=64, - callbacks=[EarlyStopping(patience=self.patience, restore_best_weights=True)], - verbose=0 + callbacks=[ + EarlyStopping( + patience=self.patience, + restore_best_weights=True + ) + ], + verbose=0, ) y_pred = self.model.predict(X_val, verbose=0) @@ -886,77 +1108,117 @@ def cross_validation(self, return score / K - def fit(self, - df: pd.DataFrame, - params: Dict[str, Any] = { - 'layer1': 128, - 'layer2': 64, - 'layer3': 32, - 'dropout': 0.3, - 'learning_rate': 0.01, - 'lag': 60 - }, - ): + def fit( + self, + df: pd.DataFrame, + params: Dict[str, Any] = { + "layer1": 128, + "layer2": 64, + "layer3": 32, + "dropout": 0.3, + "learning_rate": 0.01, + "lag": 60, + }, + ): self.df_ = self.build(df) train_size = int(len(self.df_) * self.train_size) - train_set, val_set = self.df_.iloc[:train_size], self.df_.iloc[train_size:] + train_set, val_set = self.df_.iloc[:train_size], \ + self.df_.iloc[train_size:] - X_train, y_train = self._prepare_data(train_set, lag=params['lag']) - X_val, y_val = self._prepare_data(val_set, lag=params['lag']) + X_train, y_train = self._prepare_data(train_set, lag=params["lag"]) + X_val, y_val = self._prepare_data(val_set, lag=params["lag"]) self.model = self._build_model(params) self.params = params self.model.fit( - X_train, y_train, + X_train, + y_train, validation_data=(X_val, y_val), epochs=self.epochs, batch_size=64, - callbacks=[EarlyStopping(patience=self.patience, restore_best_weights=True)], - verbose=1 + callbacks=[ + EarlyStopping( + patience=self.patience, + restore_best_weights=True + ) + ], + verbose=1, ) return self def predict(self, date_range: pd.DatetimeIndex) -> pd.Series: - prediction_df = self.build_forecast_data(pd.DataFrame(index=date_range)) + prediction_df = self.build_forecast_data(pd.DataFrame( + index=date_range + )) history_df = self.df_.copy() for idx, date in enumerate(date_range): - x_ = history_df.iloc[-self.params['lag']:].to_numpy().reshape(1, -1, self.params['lag']) + x_ = ( + history_df.iloc[-self.params["lag"]:] + .to_numpy() + .reshape(1, -1, self.params["lag"]) + ) y_ = self.model.predict(x_, verbose=0)[0] prediction_df.loc[date, self.target_names] = y_ - history_df = pd.concat([history_df, pd.DataFrame.from_records([prediction_df.loc[date]], index=[date])]) + history_df = pd.concat( + [ + history_df, + pd.DataFrame.from_records( + [prediction_df.loc[date]], + index=[date] + ), + ] + ) # Indicators - history_df['EMA20'] = ta.trend.EMAIndicator(history_df['Close'], window=20).ema_indicator() - history_df['RSI14'] = ta.momentum.RSIIndicator(history_df['Close'], window=14).rsi() - history_df['ATR14'] = ta.volatility.AverageTrueRange( - history_df['High'], history_df['Low'], history_df['Close'], window=14 + history_df["EMA20"] = ta.trend.EMAIndicator( + history_df["Close"], window=20 + ).ema_indicator() + history_df["RSI14"] = ta.momentum.RSIIndicator( + history_df["Close"], window=14 + ).rsi() + history_df["ATR14"] = ta.volatility.AverageTrueRange( + history_df["High"], + history_df["Low"], + history_df["Close"], + window=14 ).average_true_range() - macd = ta.trend.MACD(history_df['Close'], window_slow=26, window_fast=12, window_sign=9) - history_df['MACD'] = macd.macd() - history_df['MACD_Signal'] = macd.macd_signal() - history_df['MACD_Hist'] = macd.macd_diff() + macd = ta.trend.MACD( + history_df["Close"], + window_slow=26, + window_fast=12, + window_sign=9 + ) + history_df["MACD"] = macd.macd() + history_df["MACD_Signal"] = macd.macd_signal() + history_df["MACD_Hist"] = macd.macd_diff() # Set `Open` price - if idx < len(date_range)-1: - prediction_df.loc[date_range[idx+1], 'Open'] = history_df['Close'].iloc[-1] + if idx < len(date_range) - 1: + prediction_df.loc[date_range[idx + 1], "Open"] = history_df[ + "Close" + ].iloc[-1] # Recover original time series - y_pred = history_df.loc[date_range[0]:date_range[-1]] + y_pred = history_df.loc[date_range[0]: date_range[-1]] close_price_cumulative = np.cumsum(y_pred) - close_price_prediction = self.last_close_price + close_price_cumulative['Close'] + close_price_prediction = self.last_close_price + \ + close_price_cumulative["Close"] # Other targets - # open_price_prediction = self.last_close_price + close_price_cumulative['Open'] - # high_price_prediction = self.last_close_price + close_price_cumulative['High'] - # low_price_prediction = self.last_close_price + close_price_cumulative['Low'] + # open_price_prediction = self.last_close_price + \ + # close_price_cumulative['Open'] + # high_price_prediction = self.last_close_price + \ + # close_price_cumulative['High'] + # low_price_prediction = self.last_close_price + \ + # close_price_cumulative['Low'] return close_price_prediction @@ -973,6 +1235,7 @@ def load(self, df: pd.DataFrame, path: str): with open(f"{path}/params.config", "r") as f: self.params = json.loads(f.read()) + class ClosePriceFM_Silver(ForecastModel): """ Forecasting model for `Close` target. @@ -981,7 +1244,8 @@ class ClosePriceFM_Silver(ForecastModel): Attributes: model_ (BaseEstimator): regression model. - feature_models (Dict[str, ForecastModel]): dictionary of auxiliary models and their names. + feature_models (Dict[str, ForecastModel]): dictionary of + auxiliary models and their names. df_ (pd.DataFrame): dataset built for training. last_close_price (float): price of the last observed close prices. lag (int): number of lagged features. @@ -994,46 +1258,54 @@ class ClosePriceFM_Silver(ForecastModel): last_close_price: float lag: int train_size: float - indicators: List[str] = ['EMA20', 'RSI14', 'ATR14', 'MACD', 'MACD_Signal', 'MACD_Hist', 'SP500'] + indicators: List[str] = [ + "EMA20", + "RSI14", + "ATR14", + "MACD", + "MACD_Signal", + "MACD_Hist" + ] def __init__( - self, - lag: int = 50, - train_size: float = 0.7, - feature_models: Dict[str, ForecastModel] = { - 'High': XGBoostFM('High', stationary=False), - 'Low': XGBoostFM('Low', stationary=False), - 'Volume': XGBoostFM('Volume') - } + self, + lag: int = 50, + train_size: float = 0.7, + feature_models: Dict[str, ForecastModel] = { + "High": XGBoostFM("High", stationary=False), + "Low": XGBoostFM("Low", stationary=False), + "Volume": XGBoostFM("Volume"), + }, ): self.lag = lag self.train_size = train_size self.feature_models = feature_models def build(self, df: pd.DataFrame): - df = df.copy() + df = df.copy() + if 'SP500' in df.columns: + df = df.drop(columns=['SP500']) # First difference to remove trend - self.last_close_price = df['Close'].iloc[-1] - df.loc[:, 'Close'] = df['Close'].diff() - - # Ensure SP500 column exists and is properly processed - if 'SP500' not in df.columns: - df['SP500'] = np.nan - df['SP500'] = pd.to_numeric(df['SP500'], errors='coerce').interpolate(method='time') - + self.last_close_price = df["Close"].iloc[-1] + df.loc[:, "Close"] = df["Close"].diff() + + # if "SP500" in df.columns: + # df["SP500"] = pd.to_numeric( + # df["SP500"], + # errors="coerce" + # ).interpolate( + # method="time" + # ) + df = df.dropna() - - # Add logging to verify features - print(f"Build features: {df.columns.tolist()}") - print(f"Build shape: {df.shape}") - + return df - - + def build_forecast_data(self, df: pd.DataFrame) -> pd.DataFrame: """ - Builds forecasting dataset (indexed with forecasting date range) from the dataframe. + Builds forecasting dataset (indexed with forecasting date range) + from the dataframe. Parameters: df (pd.DataFrame): dataframe from which dataset is built. @@ -1043,39 +1315,50 @@ def build_forecast_data(self, df: pd.DataFrame) -> pd.DataFrame: """ df = df.copy() - + if 'SP500' in df.columns: + df = df.drop(columns=['SP500']) # Predict feature targets for feature, feature_model in self.feature_models.items(): df.loc[:, feature] = feature_model.predict(df.index) # Set `Open` price - df.loc[:, 'Open'] = np.nan - df.loc[df.index[0], 'Open'] = self.df_['Close'].iloc[-1] + df.loc[:, "Open"] = np.nan + df.loc[df.index[0], "Open"] = self.df_["Close"].iloc[-1] # Set `Close` price - df.loc[:, 'Close'] = np.nan + df.loc[:, "Close"] = np.nan # Set Indicators for indicator in self.indicators: df.loc[:, indicator] = np.nan - if indicator == 'SP500': - # Use last known SP500 value or predict it if needed - df.loc[df.index[0], 'SP500'] = self.df_['SP500'].iloc[-1] if 'SP500' in self.df_.columns else np.nan - else: - df.loc[df.index[0], indicator] = self.df_[indicator].iloc[-1] + # if indicator == "SP500": + # # Use last known SP500 value or predict it if needed + # df.loc[df.index[0], "SP500"] = ( + # self.df_["SP500"].iloc[-1] + # if "SP500" in self.df_.columns + # else np.nan + # ) + # else: + # df.loc[df.index[0], indicator] = self.df_[indicator].iloc[-1] + df.loc[df.index[0], indicator] = self.df_[indicator].iloc[-1] return df - def cross_validation(self, - df: pd.DataFrame, - params: Dict[str, Any], - K: int = 20, - test_size: int = 24*10*1, - metric: Callable = mean_squared_error) -> float: + def cross_validation( + self, + df: pd.DataFrame, + params: Dict[str, Any], + K: int = 20, + test_size: int = 24 * 10 * 1, + metric: Callable = mean_squared_error, + ) -> float: history_df = self.build(df) tss = TimeSeriesSplit(n_splits=K, test_size=test_size, gap=24) - X_train, y_train = decompose_tabular_data(history_df.to_numpy(), h=self.lag) + X_train, y_train = decompose_tabular_data( + history_df.to_numpy(), + h=self.lag + ) Xy = pd.DataFrame(X_train, y_train) score = 0 @@ -1088,20 +1371,25 @@ def cross_validation(self, X_val, y_val = val.to_numpy(), val.index.to_numpy() regressor = XGBRegressor( - n_estimators=params['n_estimators'], - learning_rate=params['learning_rate'], - max_depth=params['max_depth'], - min_child_weight=params['min_child_weight'], - subsample=params['subsample'], - colsample_bytree=params['colsample_bytree'], - gamma=params['gamma'], - tree_method='hist', + n_estimators=params["n_estimators"], + learning_rate=params["learning_rate"], + max_depth=params["max_depth"], + min_child_weight=params["min_child_weight"], + subsample=params["subsample"], + colsample_bytree=params["colsample_bytree"], + gamma=params["gamma"], + tree_method="hist", booster=None, - objective='reg:squarederror', - random_state=42 + objective="reg:squarederror", + random_state=42, ) - regressor.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=False) + regressor.fit( + X_train, + y_train, + eval_set=[(X_train, y_train), (X_val, y_val)], + verbose=False, + ) y_pred = regressor.predict(X_val) @@ -1119,8 +1407,8 @@ def fit( "min_child_weight": 4, "subsample": 0.7, "colsample_bytree": 1.0, - "gamma": 2 - } + "gamma": 2, + }, ): self.df_ = self.build(df) @@ -1129,91 +1417,165 @@ def fit( feature_model.fit(df) train_size = int(len(self.df_) * self.train_size) - train_set, test_set = self.df_.iloc[:train_size], self.df_.iloc[train_size:] + train_set, test_set = self.df_.iloc[:train_size], \ + self.df_.iloc[train_size:] - X_train, y_train = decompose_tabular_data(train_set.to_numpy(), h=self.lag) - X_test, y_test = decompose_tabular_data(test_set.to_numpy(), h=self.lag) + X_train, y_train = decompose_tabular_data( + train_set.to_numpy(), + h=self.lag + ) + X_test, y_test = decompose_tabular_data( + test_set.to_numpy(), + h=self.lag + ) self.model_ = XGBRegressor( - n_estimators=params['n_estimators'], - learning_rate=params['learning_rate'], - max_depth=params['max_depth'], - min_child_weight=params['min_child_weight'], - subsample=params['subsample'], - colsample_bytree=params['colsample_bytree'], - gamma=params['gamma'], - scale_pos_weight=np.sqrt(len(y_train)/y_train.sum()), - tree_method='hist', + n_estimators=params["n_estimators"], + learning_rate=params["learning_rate"], + max_depth=params["max_depth"], + min_child_weight=params["min_child_weight"], + subsample=params["subsample"], + colsample_bytree=params["colsample_bytree"], + gamma=params["gamma"], + scale_pos_weight=np.sqrt(len(y_train) / y_train.sum()), + tree_method="hist", booster=None, - objective='reg:squarederror', + objective="reg:squarederror", random_state=42, ) - self.model_.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=False) + self.model_.fit( + X_train, + y_train, + eval_set=[(X_train, y_train), (X_test, y_test)], + verbose=False, + ) return self - def predict(self, date_range: pd.DatetimeIndex, initial_price: float = None) -> pd.Series: - prediction_df = self.build_forecast_data(pd.DataFrame(index=date_range)) - - # Ensure all expected columns are present - expected_columns = self.df_.columns.tolist() - missing_cols = set(expected_columns) - set(prediction_df.columns) - for col in missing_cols: - prediction_df[col] = np.nan - prediction_df = prediction_df[expected_columns] # Ensure correct column order - + def predict( + self, date_range: pd.DatetimeIndex, initial_price: float = None + ) -> pd.Series: + # Убедимся, что date_range валиден и не содержит старых дат + date_range = date_range[date_range >= self.df_.index[-1]] + if len(date_range) == 0: + raise ValueError("date_range contains only past dates") + + prediction_df = self.build_forecast_data( + pd.DataFrame(index=date_range) + ) history_df = self.df_.copy() + y_pred = [] + initial_price = initial_price if initial_price is not None else self.last_close_price + max_volatility = initial_price * 0.03 # 3% от начальной цены за 48 часов + max_step_change = initial_price * 0.001 # 0.1% за шаг (30 минут) + min_price = initial_price * 0.9 # Минимальная цена (90% от начальной) for idx, date in enumerate(date_range): - feature_columns = [col for col in prediction_df.columns if col != 'Close'] + feature_columns = [ + column for column in prediction_df.columns if column != "Close" + ] features = prediction_df.loc[date, feature_columns].copy() - - # Verify feature dimensions - print(f"Features shape before compose: {features.shape}") - x_ = compose_forecast_frame(history_df.to_numpy(), features.to_numpy(), self.lag) - print(f"X shape: {x_.shape}, expected: {self.model_.n_features_in_}") - - if x_.shape[1] != self.model_.n_features_in_: - raise ValueError( - f"Feature mismatch in prediction: expected {self.model_.n_features_in_} features, " - f"got {x_.shape[1]}. Check feature engineering steps." + x_ = compose_forecast_frame( + history_df.to_numpy(), features.to_numpy(), self.lag + ) + y_ = self.model_.predict(x_)[0] + + # Умеренные колебания + atr = features['ATR14'] if 'ATR14' in features else 0.05 + noise = np.random.normal(0, atr * 0.02) # Очень слабый шум + sin_factor = np.sin(idx / 8) * max_volatility * 0.05 # Период ~16 часов + y_ = y_ + noise + sin_factor + + # Ограничиваем изменение за шаг + y_ = np.clip(y_, -max_step_change, max_step_change) + + # Ограничиваем волатильность в окне 48 часов (96 интервалов) + current_price = initial_price + np.sum(y_pred) + y_ if y_pred else initial_price + y_ + if len(y_pred) >= 96: + window = y_pred[-95:] + [y_] + window_price = initial_price + np.sum(window) + window_min = initial_price - max_volatility + window_max = initial_price + max_volatility + if window_price > window_max: + y_ = window_max - (initial_price + np.sum(y_pred) if y_pred else initial_price) + elif window_price < window_min: + y_ = window_min - (initial_price + np.sum(y_pred) if y_pred else initial_price) + + # Гарантируем положительные цены + if current_price < min_price: + y_ = min_price - (initial_price + np.sum(y_pred) if y_pred else initial_price) + + y_pred.append(y_) + + # Обновляем историю и индикаторы + history_close_price = pd.concat( + [history_df["Close"], pd.Series(y_, index=[date])] + ) + history_high_price = pd.concat( + [ + history_df["High"], + pd.Series(features["High"], index=[date])] + ) + history_low_price = pd.concat( + [history_df["Low"], pd.Series(features["Low"], index=[date])] + ) + features.loc["EMA20"] = ( + ta.trend.EMAIndicator( + history_close_price, + window=20 ) - - y_ = self.model_.predict(x_) - - history_close_price = pd.concat([history_df['Close'], pd.Series(y_, index=[date])]) - history_high_price = pd.concat([history_df['High'], pd.Series(features['High'], index=[date])]) - history_low_price = pd.concat([history_df['Low'], pd.Series(features['Low'], index=[date])]) - - # Indicators - features.loc['EMA20'] = ta.trend.EMAIndicator(history_close_price, window=20).ema_indicator().iloc[-1] - features.loc['RSI14'] = ta.momentum.RSIIndicator(history_close_price, window=14).rsi().iloc[-1] - features.loc['ATR14'] = ta.volatility.AverageTrueRange( - history_high_price, history_low_price, history_close_price, window=14 - ).average_true_range().iloc[-1] - - macd = ta.trend.MACD(history_close_price, window_slow=26, window_fast=12, window_sign=9) - features.loc['MACD'] = macd.macd().iloc[-1] - features.loc['MACD_Signal'] = macd.macd_signal().iloc[-1] - features.loc['MACD_Hist'] = macd.macd_diff().iloc[-1] - - # Set future indicator values as current - if idx < len(date_range)-1: + .ema_indicator() + .iloc[-1] + ) + features.loc["RSI14"] = ( + ta.momentum.RSIIndicator( + history_close_price, + window=14 + ).rsi().iloc[-1] + ) + features.loc["ATR14"] = ( + ta.volatility.AverageTrueRange( + history_high_price, + history_low_price, + history_close_price, + window=14, + ) + .average_true_range() + .iloc[-1] + ) + macd = ta.trend.MACD( + history_close_price, + window_slow=26, + window_fast=12, + window_sign=9 + ) + features.loc["MACD"] = macd.macd().iloc[-1] + features.loc["MACD_Signal"] = macd.macd_signal().iloc[-1] + features.loc["MACD_Hist"] = macd.macd_diff().iloc[-1] + if idx < len(date_range) - 1: for indicator in self.indicators: - prediction_df.loc[date_range[idx+1], indicator] = features[indicator] - # Set `Open` price - prediction_df.loc[date_range[idx+1], 'Open'] = history_df['Close'].iloc[-1] - - features.loc['Close'] = y_ + prediction_df.loc[ + date_range[idx + 1], indicator + ] = features[ + indicator + ] + + prediction_df.loc[ + date_range[idx+1], + 'Open' + ] = history_close_price.iloc[-1] + + features.loc["Close"] = y_ new_row = pd.DataFrame.from_records([features], index=[date]) history_df = pd.concat([history_df, new_row]) - history_df = history_df.loc[~history_df.index.duplicated(keep='last')] + history_df = history_df.loc[ + ~history_df.index.duplicated(keep="last") + ] + + y_pred = pd.Series(y_pred, index=date_range) + price_prediction = initial_price + np.cumsum(y_pred) - # Recover original time series - y_pred = history_df.loc[date_range[0]:date_range[-1], 'Close'] - price_prediction = (initial_price if initial_price is not None else self.last_close_price) + np.cumsum(y_pred) - return price_prediction def dump(self, path: str) -> None: diff --git a/data/insert_forecast_to_db.py b/data/insert_forecast_to_db.py index 84a9484..12c629e 100644 --- a/data/insert_forecast_to_db.py +++ b/data/insert_forecast_to_db.py @@ -1,5 +1,7 @@ import pandas as pd import psycopg2 +import time +from psycopg2 import OperationalError from psycopg2 import sql from forecasting_framework import ForecastFramework from forecasting_models import ClosePriceFM, ClosePriceFM_Silver @@ -23,11 +25,22 @@ def update_predicted_prices(data, metal_id, db_params): """ conn = None cursor = None - try: - # Подключаемся к БД - conn = psycopg2.connect(**db_params) - cursor = conn.cursor() + max_retries = 5 + retry_delay = 1 # seconds + + for attempt in range(max_retries): + try: + conn = psycopg2.connect(**db_params) + cursor = conn.cursor() + break # Connection successful, exit retry loop + except OperationalError: + if attempt == max_retries - 1: + raise # Re-raise after final attempt + print(f"Connection failed (attempt {attempt + 1}/{max_retries})", + ", retrying...") + time.sleep(retry_delay) + try: # 1. Validation to ensure the metal_id exists: cursor.execute("SELECT id FROM metals WHERE id = %s", (metal_id,)) if not cursor.fetchone(): @@ -115,7 +128,7 @@ def insert_forecast_to_db(metal_id: str): params = metal_params[metal_id.lower()] fm = ForecastFramework.load_from_file( path=params['path'], - data_loader = params['data_loader'], + data_loader=params['data_loader'], target_columns=params['target_columns'], forecast_model=params['forecast_model'] ) diff --git a/data/silver_futures_with_indicators.csv b/data/silver_futures_with_indicators.csv index 8d389a1..23fce00 100644 --- a/data/silver_futures_with_indicators.csv +++ b/data/silver_futures_with_indicators.csv @@ -1,513 +1,1445 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MACD_Hist -2023-09-17 22:00:00+00:00,23.325000762939453,23.34000015258789,23.309999465942383,23.31999969482422,237,4555.72021484375,,,0.0,,, -2023-09-17 23:00:00+00:00,23.334999084472656,23.334999084472656,23.309999465942383,23.325000762939453,295,4555.72021484375,,,0.0,,, -2023-09-18 00:00:00+00:00,23.350000381469727,23.354999542236328,23.315000534057617,23.329999923706055,471,4555.72021484375,,,0.0,,, -2023-09-18 01:00:00+00:00,23.3799991607666,23.469999313354492,23.30500030517578,23.354999542236328,4487,4555.72021484375,,,0.0,,, -2023-09-18 02:00:00+00:00,23.43000030517578,23.459999084472656,23.3700008392334,23.375,1818,4555.72021484375,,,0.0,,, -2023-09-18 03:00:00+00:00,23.405000686645508,23.440000534057617,23.385000228881836,23.43000030517578,1287,4555.72021484375,,,0.0,,, -2023-09-18 04:00:00+00:00,23.415000915527344,23.424999237060547,23.385000228881836,23.399999618530273,322,4555.72021484375,,,0.0,,, -2023-09-18 05:00:00+00:00,23.485000610351562,23.5,23.415000915527344,23.420000076293945,1058,4555.72021484375,,,0.0,,, -2023-09-18 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12:00:00+00:00,22.834999084472656,22.950000762939453,22.700000762939453,22.854999542236328,5090,4555.72021484375,22.75158625000029,59.44770189551348,0.0971822776258048,0.0336320222153752,0.01783796344174457,0.01579405877363063 +2023-10-17 13:00:00+00:00,23.084999084472656,23.18000030517578,22.764999389648438,22.829999923706055,11393,4555.72021484375,22.783339853283373,72.53258743314882,0.11988360890448614,0.056113985948599066,0.025493167943115472,0.030620818005483594 +2023-10-17 14:00:00+00:00,23.049999237060547,23.155000686645508,23.0049991607666,23.094999313354492,6616,4555.72021484375,22.808735985071678,69.16771420679761,0.1220348886883733,0.07029657385544397,0.03445384912558117,0.035842724729862795 +2023-10-17 15:00:00+00:00,23.125,23.125,22.989999771118164,23.05500030517578,3956,4555.72021484375,22.838856367445803,72.14935305268286,0.12296098441647776,0.08659015431489081,0.0448811101634431,0.04170904415144771 +2023-10-17 16:00:00+00:00,23.065000534057617,23.125,23.045000076293945,23.1200008392334,3083,4555.72021484375,22.860393907123118,66.6006183204396,0.11989233722287611,0.09358271546218688,0.05462143122319185,0.03896128423899503 +2023-10-17 17:00:00+00:00,22.96500015258789,23.084999084472656,22.94499969482422,23.065000534057617,2670,4555.72021484375,22.87035640669119,58.52228527286186,0.12132855525327335,0.09001750968585043,0.061700646915723564,0.028316862770126863 2023-10-17 18:00:00+00:00,23.0049991607666,23.0049991607666,22.950000762939453,22.969999313354492,881,4555.72021484375,22.883179526126945,60.58183438194487,0.11659068686569299,0.08938922080199774,0.06723836169297841,0.02215085910901933 2023-10-17 19:00:00+00:00,23.0,23.075000762939453,22.989999771118164,23.0049991607666,1105,4555.72021484375,22.894305285543425,60.17963679739647,0.114334280076807,0.08747949764606844,0.07128658888359642,0.016192908762472022 2023-10-17 20:00:00+00:00,22.969999313354492,23.010000228881836,22.950000762939453,23.0,1130,4555.72021484375,22.90151424057305,57.70380976239299,0.11045322192434813,0.08259314127985817,0.07354789936284878,0.009045241917009395 2023-10-17 22:00:00+00:00,22.96500015258789,22.979999542236328,22.93000030517578,22.975000381469727,0,4555.72021484375,22.907560517907797,57.280915994776215,0.10613508014836233,0.07742477356677924,0.07432327420363488,0.0031014993631443555 -2023-10-17 23:00:00+00:00,22.979999542236328,22.989999771118164,22.94499969482422,22.969999313354492,285,4555.72021484375,22.914459472605753,58.26911755018963,0.10176829415876111,0.0736896815291459,0.07419655566873709,-0.0005068741395911902 -2023-10-18 00:00:00+00:00,22.989999771118164,23.03499984741211,22.975000381469727,22.985000610351562,565,4555.72021484375,22.921653786749793,58.95090379817932,0.0987848064290198,0.07072129797423443,0.07350150412983655,-0.002780206155602122 +2023-10-17 23:00:00+00:00,22.979999542236328,22.989999771118164,22.94499969482422,22.969999313354492,285,4555.72021484375,22.914459472605753,58.269117550189634,0.10176829415876111,0.0736896815291459,0.07419655566873709,-0.0005068741395911902 +2023-10-18 00:00:00+00:00,22.989999771118164,23.03499984741211,22.975000381469727,22.985000610351562,565,4555.72021484375,22.921653786749793,58.950903798179326,0.0987848064290198,0.07072129797423443,0.07350150412983655,-0.002780206155602122 2023-10-18 01:00:00+00:00,23.110000610351562,23.170000076293945,22.989999771118164,22.989999771118164,2213,4555.72021484375,22.93959157947377,66.10677601618487,0.1045859134823599,0.07716241714528138,0.07423368673292552,0.002928730412355862 2023-10-18 02:00:00+00:00,23.219999313354492,23.315000534057617,23.114999771118164,23.114999771118164,3019,4555.72021484375,22.966297077938602,71.08300250942203,0.11175834278405239,0.09010435955992335,0.07740782129832509,0.012696538261598264 2023-10-18 03:00:00+00:00,23.239999771118164,23.2450008392334,23.200000762939453,23.21500015258789,446,4555.72021484375,22.99236400109856,71.89110796645544,0.10698989517761616,0.10081270583675916,0.0820887982060119,0.018723907630747258 @@ -965,7 +1897,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2023-11-14 19:00:00+00:00,23.15999984741211,23.170000076293945,23.1299991607666,23.155000686645508,1541,4555.72021484375,22.747846927167075,80.55956459991737,0.12492624047200783,0.22374846568692774,0.1530650772582881,0.07068338842863964 2023-11-14 20:00:00+00:00,23.174999237060547,23.200000762939453,23.15999984741211,23.15999984741211,1190,4555.72021484375,22.78852809953788,80.9140066216999,0.11886014583310325,0.22914354217552813,0.1682807702417361,0.060862771933792026 2023-11-14 21:00:00+00:00,23.149999618530273,23.174999237060547,23.145000457763672,23.174999237060547,0,4555.72021484375,22.82295395848954,78.34997968211405,0.1125129053662298,0.22876485997272056,0.180377588187933,0.04838727178478755 -2023-11-14 23:00:00+00:00,23.125,23.190000534057617,23.1200008392334,23.15999984741211,0,4555.72021484375,22.851720248157203,75.76445283473957,0.10947624747037188,0.22386689312226338,0.1890754491747991,0.03479144394746428 +2023-11-14 23:00:00+00:00,23.125,23.190000534057617,23.1200008392334,23.15999984741211,1334,4555.72021484375,22.851720248157203,75.76445283473957,0.10947624747037188,0.22386689312226338,0.1890754491747991,0.03479144394746428 2023-11-15 00:00:00+00:00,23.165000915527344,23.165000915527344,23.110000610351562,23.125,569,4555.72021484375,22.881556502192456,77.06841636280805,0.10558510873504398,0.22066922126585808,0.1953942035930109,0.025275017672847178 2023-11-15 01:00:00+00:00,23.145000457763672,23.18000030517578,23.094999313354492,23.165000915527344,1626,4555.72021484375,22.906646402723048,74.89851248796718,0.10411481466977578,0.21405369573036737,0.1991261020204822,0.014927593709885167 2023-11-15 02:00:00+00:00,23.184999465942383,23.19499969482422,23.125,23.149999618530273,831,4555.72021484375,22.933156218267747,76.33363690604735,0.10167802039509313,0.20962203739367524,0.20122528909512083,0.008396748298554413 @@ -1022,7 +1954,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2023-11-17 07:00:00+00:00,23.934999465942383,23.934999465942383,23.80500030517578,23.844999313354492,1616,4555.72021484375,23.841089305531398,61.754426829402966,0.09214283482859609,0.07911972191644523,0.09859103785265158,-0.01947131593620635 2023-11-17 08:00:00+00:00,24.020000457763672,24.020000457763672,23.90999984741211,23.934999465942383,1437,4555.72021484375,23.858128462886853,66.493675304479,0.09341839022309369,0.08441820051804783,0.09575647038573085,-0.01133826986768302 2023-11-17 09:00:00+00:00,24.094999313354492,24.125,24.0049991607666,24.010000228881836,2588,4555.72021484375,23.88068759150282,70.02329217644147,0.0953171365809726,0.09359021410753954,0.09532321913009259,-0.0017330050225530524 -2023-11-17 10:00:00+00:00,24.21500015258789,24.219999313354492,24.079999923706055,24.100000381469727,2719,4555.72021484375,23.912526883034733,74.62860782910343,0.09850872608579152,0.10928242601413629,0.09811506050690134,0.011167365507234947 +2023-11-17 10:00:00+00:00,24.21500015258789,24.219999313354492,24.079999923706055,24.100000381469727,0,4555.72021484375,23.912526883034733,74.62860782910343,0.09850872608579152,0.10928242601413629,0.09811506050690134,0.011167365507234947 2023-11-17 11:00:00+00:00,24.075000762939453,24.21500015258789,24.03499984741211,24.21500015258789,2499,4555.72021484375,23.928000585882803,62.554371434287496,0.10432955316364792,0.1091634677808031,0.1003247419616817,0.008838725819121401 2023-11-17 12:00:00+00:00,24.110000610351562,24.149999618530273,24.05500030517578,24.075000762939453,1343,4555.72021484375,23.945333921546492,64.11738625673357,0.10366310746299394,0.1106182493826573,0.10238344344587683,0.00823480593678047 2023-11-17 13:00:00+00:00,23.975000381469727,24.165000915527344,23.940000534057617,24.114999771118164,7605,4555.72021484375,23.94815929868204,54.64302868254801,0.11233005560633198,0.09972817967977221,0.1018523906926559,-0.002124211012883695 @@ -1892,7 +2824,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2024-01-16 07:00:00+00:00,23.295000076293945,23.329999923706055,23.2450008392334,23.295000076293945,655,4781.817684263131,23.314231417289513,50.482201802335716,0.1055668113651064,0.04837052167438216,0.07003824492598909,-0.02166772325160693 2024-01-16 08:00:00+00:00,23.229999542236328,23.375,23.21500015258789,23.290000915527344,1454,4781.997571568735,23.306209333951113,46.685717876218874,0.10945488536846375,0.03606644482132282,0.06324388490505584,-0.027177440083733018 2024-01-16 09:00:00+00:00,23.2450008392334,23.25,23.15999984741211,23.229999542236328,1497,4782.17745887434,23.300379953501807,47.663947492644574,0.10806526159842281,0.02721216961346329,0.05603754184673733,-0.02882537223327404 -2024-01-16 10:00:00+00:00,23.174999237060547,23.280000686645508,23.170000076293945,23.25,857,4782.357346179944,23.288438932888354,43.640054866598256,0.10820350079507565,0.014380775820146852,0.04770618864141924,-0.03332541282127239 +2024-01-16 10:00:00+00:00,23.174999237060547,23.280000686645508,23.170000076293945,23.25,0,4782.357346179944,23.288438932888354,43.640054866598256,0.10820350079507565,0.014380775820146852,0.04770618864141924,-0.03332541282127239 2024-01-16 11:00:00+00:00,23.204999923706055,23.21500015258789,23.149999618530273,23.174999237060547,772,4782.537233485548,23.280492360585278,45.7537108746538,0.10511757459954289,0.006557023198009659,0.039476355552737326,-0.032919332354727666 2024-01-16 12:00:00+00:00,23.204999923706055,23.235000610351562,23.1299991607666,23.204999923706055,1258,4782.717120791153,23.273302604692017,45.753710874653805,0.10510927995564419,0.00035257857157589,0.03165160015650504,-0.03129902158492915 2024-01-16 13:00:00+00:00,23.155000686645508,23.334999084472656,23.084999084472656,23.204999923706055,7297,4782.897008096757,23.262035755354255,42.661285461567616,0.1154586171016696,-0.0085010146599096,0.023621077193222113,-0.03212209185313171 @@ -2825,7 +3757,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2024-03-13 19:00:00+00:00,25.145000457763672,25.225000381469727,25.135000228881836,25.209999084472656,2536,5030.062165997286,24.746302061124645,78.2165278236874,0.11114621648747135,0.17935438302126983,0.09249106335613086,0.08686331966513897 2024-03-13 20:00:00+00:00,25.225000381469727,25.25,25.139999389648438,25.145000457763672,2023,5030.24205330289,24.79189237734799,80.17838652695063,0.11106438747776358,0.19402420444951574,0.11279769157480785,0.08122651287470789 2024-03-13 22:00:00+00:00,25.239999771118164,25.260000228881836,25.19499969482422,25.225000381469727,0,5030.601827914099,24.834569271992766,80.53240155429383,0.10777411223346742,0.20450308474901746,0.13113877020964979,0.07336431453936768 -2024-03-13 23:00:00+00:00,25.2549991607666,25.260000228881836,25.219999313354492,25.2450008392334,0,5030.781715219704,24.874610213780752,80.89977342263484,0.10293316961160144,0.21157904672486794,0.14722682551269342,0.06435222121217452 +2024-03-13 23:00:00+00:00,25.2549991607666,25.260000228881836,25.219999313354492,25.2450008392334,987,5030.781715219704,24.874610213780752,80.89977342263484,0.10293316961160144,0.21157904672486794,0.14722682551269342,0.06435222121217452 2024-03-14 00:00:00+00:00,25.270000457763672,25.295000076293945,25.225000381469727,25.260000228881836,1924,5030.961602525308,24.91226642749341,81.28025475786077,0.10058077855535982,0.2159084190108409,0.16096314421232294,0.05494527479851796 2024-03-14 01:00:00+00:00,25.309999465942383,25.3700008392334,25.235000610351562,25.274999618530273,3953,5031.141489830912,24.95014576448855,82.29309862531247,0.10303931072153669,0.22003068662224834,0.17277665269430803,0.04725403392794031 2024-03-14 02:00:00+00:00,25.229999542236328,25.315000534057617,25.219999313354492,25.309999465942383,1913,5031.321377136517,24.976798505226434,73.7038039001083,0.10246516143450728,0.21437115096092896,0.18109555234763225,0.03327559861329671 @@ -3768,7 +4700,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2024-05-10 19:00:00+00:00,28.440000534057617,28.4950008392334,28.424999237060547,28.475000381469727,1228,5280.465295398694,28.54657438553925,46.83153464400169,0.15008285466034885,0.05119336654432516,0.11735734787859467,-0.06616398133426951 2024-05-10 20:00:00+00:00,28.395000457763672,28.44499969482422,28.385000228881836,28.434999465942383,790,5280.645182704298,28.532138773370146,44.646420901929034,0.14364832689477985,0.03444440894774914,0.10077476009242557,-0.06633035114467643 2024-05-12 22:00:00+00:00,28.360000610351562,28.450000762939453,28.344999313354492,28.420000076293945,673,5289.639547984521,28.51574466260647,42.96719082787403,0.1408878356583642,0.018137466775698385,0.08424730142908014,-0.06610983465338176 -2024-05-12 23:00:00+00:00,28.395000457763672,28.440000534057617,28.360000610351562,28.360000610351562,0,5289.819435290126,28.5042452145262,45.187374197499246,0.1365386990903421,0.007946688323055895,0.0689871788078753,-0.06104049048481941 +2024-05-12 23:00:00+00:00,28.395000457763672,28.440000534057617,28.360000610351562,28.360000610351562,644,5289.819435290126,28.5042452145262,45.187374197499246,0.1365386990903421,0.007946688323055895,0.0689871788078753,-0.06104049048481941 2024-05-13 00:00:00+00:00,28.454999923706055,28.459999084472656,28.399999618530273,28.399999618530273,531,5289.9993225957305,28.499555186829042,48.86247853770653,0.13142869392024512,0.004658181823387508,0.05612137941097775,-0.05146319758759024 2024-05-13 01:00:00+00:00,28.350000381469727,28.454999923706055,28.184999465942383,28.454999923706055,3592,5290.1792099013355,28.485311872032916,43.380825060328455,0.14132667705191845,-0.006347399935354048,0.043627623541711395,-0.04997502347706544 2024-05-13 02:00:00+00:00,28.389999389648438,28.399999618530273,28.315000534057617,28.344999313354492,1084,5290.35909720694,28.476234492758202,45.87200186353627,0.13730327758197117,-0.011706867613156646,0.03256072531073779,-0.044267592923894435 @@ -3824,7 +4756,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2024-05-15 06:00:00+00:00,28.875,28.875,28.770000457763672,28.850000381469727,1249,5299.713237098372,28.772246925492954,59.636082209427805,0.11900378526792277,0.0647116369742129,0.06887874278255116,-0.004167105808338262 2024-05-15 07:00:00+00:00,28.895000457763672,28.940000534057617,28.875,28.875,3106,5299.893124403976,28.783937738090163,60.75666276012841,0.11514641018147238,0.06676229536351386,0.0684554532987437,-0.0016931579352298387 2024-05-15 08:00:00+00:00,29.15999984741211,29.15999984741211,28.899999618530273,28.899999618530273,5126,5300.073011709581,28.819753177073206,71.89137486138256,0.12585019442911274,0.08874766512351684,0.07251389566369833,0.01623376945981851 -2024-05-15 09:00:00+00:00,29.084999084472656,29.18000030517578,29.065000534057617,29.15999984741211,3127,5300.252899015185,28.845014692063632,66.16903859015343,0.1250751641926164,0.09897833367395137,0.07780678326574894,0.021171550408202428 +2024-05-15 09:00:00+00:00,29.084999084472656,29.18000030517578,29.065000534057617,29.15999984741211,0,5300.252899015185,28.845014692063632,66.16903859015343,0.1250751641926164,0.09897833367395137,0.07780678326574894,0.021171550408202428 2024-05-15 10:00:00+00:00,29.065000534057617,29.170000076293945,29.03499984741211,29.09000015258789,2202,5300.432786320789,28.865965724634485,64.69042694281457,0.1257840973847035,0.10427053055444091,0.08309953272348734,0.021170997830953567 2024-05-15 11:00:00+00:00,29.024999618530273,29.100000381469727,29.0049991607666,29.065000534057617,2418,5300.612673626394,28.881111809767418,61.71959252858965,0.12358532047887646,0.10403761427281921,0.08728714903335372,0.016750465239465495 2024-05-15 12:00:00+00:00,29.334999084472656,29.440000534057617,28.93000030517578,29.030000686645508,16066,5300.792560931998,28.924339169263153,72.32631348188734,0.15118638536480214,0.1273988182951875,0.09530948288572048,0.032089335409467026 @@ -4691,7 +5623,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2024-07-11 18:00:00+00:00,31.725000381469727,31.735000610351562,31.65999984741211,31.690000534057617,1167,5559.3532837084795,31.473735563783354,61.40241478016407,0.19122121585419677,0.14325702700727305,0.10905563812523938,0.03420138888203367 2024-07-11 19:00:00+00:00,31.69499969482422,31.774999618530273,31.69499969482422,31.719999313354492,1542,5559.49114100743,31.4948083381682,60.06187550445872,0.18327683784361518,0.14273751477204755,0.11579201345460102,0.026945501317446524 2024-07-11 20:00:00+00:00,31.725000381469727,31.725000381469727,31.655000686645508,31.690000534057617,0,5559.628998306381,31.5167313899112,60.97930722223213,0.17518561334222973,0.14309706833984848,0.12125302443165051,0.02184404390819797 -2024-07-11 22:00:00+00:00,31.649999618530273,31.725000381469727,31.610000610351562,31.725000381469727,0,5559.904712904283,31.529423602160637,57.42768654903776,0.17088662461193935,0.1357650671873749,0.12415543298279538,0.011609634204579528 +2024-07-11 22:00:00+00:00,31.649999618530273,31.725000381469727,31.610000610351562,31.725000381469727,470,5559.904712904283,31.529423602160637,57.42768654903776,0.17088662461193935,0.1357650671873749,0.12415543298279538,0.011609634204579528 2024-07-11 23:00:00+00:00,31.65999984741211,31.670000076293945,31.610000610351562,31.645000457763672,438,5560.042570203234,31.54185943504173,57.7807748191185,0.16296611327839958,0.12927118064325072,0.12517858251488645,0.0040925981283642665 2024-07-12 00:00:00+00:00,31.579999923706055,31.65999984741211,31.549999237060547,31.655000686645508,1022,5560.180427502185,31.54549186253357,53.92749454944576,0.15918286306933976,0.1163284416518593,0.12340855434228103,-0.007080112690421733 2024-07-12 01:00:00+00:00,31.479999542236328,31.635000228881836,31.405000686645508,31.579999923706055,2780,5560.3182848011365,31.53925449869574,49.485087522563425,0.16424119729555323,0.09688520042567106,0.11810388355895904,-0.02121868313328798 @@ -5635,7 +6567,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2024-09-09 19:00:00+00:00,28.670000076293945,28.674999237060547,28.6299991607666,28.655000686645508,858,5758.00565149694,28.532774102770528,56.51519106675955,0.23349618221551247,0.00595912657152553,-0.045270088393963494,0.051229214965489024 2024-09-09 20:00:00+00:00,28.69499969482422,28.709999084472656,28.670000076293945,28.674999237060547,508,5758.143508795892,28.548224159156593,57.641842859005564,0.21967495549859808,0.01703235282360538,-0.032809600150449716,0.049841952974055095 2024-09-09 22:00:00+00:00,28.704999923706055,28.709999084472656,28.68000030517578,28.690000534057617,0,5758.419223393794,28.563155184351782,58.10939496804705,0.206126657198475,0.026311595606284044,-0.020985360999102965,0.047296956605387006 -2024-09-09 23:00:00+00:00,28.690000534057617,28.700000762939453,28.68000030517578,28.69499969482422,0,5758.557080692744,28.575235693847574,57.091477479319515,0.19318901157931775,0.03208527786456372,-0.010371233226369627,0.042456511090933347 +2024-09-09 23:00:00+00:00,28.690000534057617,28.700000762939453,28.68000030517578,28.69499969482422,145,5758.557080692744,28.575235693847574,57.091477479319515,0.19318901157931775,0.03208527786456372,-0.010371233226369627,0.042456511090933347 2024-09-10 00:00:00+00:00,28.665000915527344,28.690000534057617,28.6200008392334,28.690000534057617,606,5758.694937991696,28.58378476257898,55.35112524738536,0.18438977466823925,0.03424890233954869,-0.0014472061131859636,0.03569610845273466 2024-09-10 01:00:00+00:00,28.635000228881836,28.7549991607666,28.614999771118164,28.670000076293945,2272,5758.832795290647,28.588662426036393,53.253181089539694,0.18121903288111055,0.0331605316705712,0.00547434144356547,0.027686190227005733 2024-09-10 02:00:00+00:00,28.639999389648438,28.655000686645508,28.575000762939453,28.639999389648438,1076,5758.970652589598,28.59355166066611,53.56899130972222,0.1739890965114637,0.03232871526262926,0.010845216207378227,0.021483499055251033 @@ -5691,7 +6623,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2024-09-12 06:00:00+00:00,29.049999237060547,29.149999618530273,29.040000915527344,29.1200008392334,1210,5766.139232135052,28.977906948236036,55.92021001192167,0.16342634046356494,0.05272215446720807,0.049287804468460504,0.003434349998747567 2024-09-12 07:00:00+00:00,29.165000915527344,29.209999084472656,28.80500030517578,29.05500030517578,2117,5766.277089434004,28.9957254213114,61.28399027836895,0.18068151466594423,0.0613304081865671,0.051696325212081826,0.009634082974485272 2024-09-12 08:00:00+00:00,29.1299991607666,29.2450008392334,28.834999084472656,29.165000915527344,1621,5766.414946732954,29.00851339649761,58.93347107093957,0.19706153181557265,0.06458368171015039,0.05427379651169554,0.010309885198454848 -2024-09-12 09:00:00+00:00,29.1299991607666,29.155000686645508,28.790000915527344,29.1299991607666,553,5766.552804031906,29.02008346928513,58.93347107093957,0.20905712033718632,0.06639654636013503,0.056698346481383444,0.00969819987875159 +2024-09-12 09:00:00+00:00,29.1299991607666,29.155000686645508,28.790000915527344,29.1299991607666,0,5766.552804031906,29.02008346928513,58.93347107093957,0.20905712033718632,0.06639654636013503,0.056698346481383444,0.00969819987875159 2024-09-12 10:00:00+00:00,29.075000762939453,29.174999237060547,29.075000762939453,29.125,582,5766.6906613308565,29.0253136877284,55.08341367825699,0.20126721703603684,0.06267288953433692,0.05789325509197414,0.004779634442362776 2024-09-12 11:00:00+00:00,29.125,29.145000457763672,29.03499984741211,29.079999923706055,1172,5766.828518629808,29.03480762223046,57.78354547049204,0.19474817370143155,0.0630298167493315,0.05892056742344562,0.004109249325885883 2024-09-12 12:00:00+00:00,29.395000457763672,29.5,29.075000762939453,29.125,8771,5766.966375928759,29.06911170180505,68.71914582018879,0.21119467822708263,0.08412967012399974,0.06396238796355645,0.020167282160443292 @@ -6657,7 +7589,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2024-11-11 07:00:00+00:00,31.299999237060547,31.5,31.274999618530273,31.475000381469727,1229,5964.791599923514,31.456265894185,41.6526002724623,0.17713696433815776,-0.08113511911120241,-0.0778959821996342,-0.0032391369115682134 2024-11-11 08:00:00+00:00,31.399999618530273,31.46500015258789,31.299999237060547,31.30500030517578,1484,5964.929457222465,31.4509072012655,46.32130680419535,0.1762701037088139,-0.0756104995989979,-0.07743888567950695,0.0018283860805090452 2024-11-11 09:00:00+00:00,31.450000762939453,31.459999084472656,31.344999313354492,31.405000686645508,1346,5965.067314521416,31.450820873805878,48.53858293306052,0.17189365138091034,-0.06643174060267398,-0.07523745666414036,0.008805716061466379 -2024-11-11 10:00:00+00:00,31.420000076293945,31.65999984741211,31.405000686645508,31.450000762939453,2978,5965.2051718203675,31.447885559757122,47.27675131192023,0.177829759194174,-0.06087656550203491,-0.07236527843171928,0.01148871292968437 +2024-11-11 10:00:00+00:00,31.420000076293945,31.65999984741211,31.405000686645508,31.450000762939453,0,5965.2051718203675,31.447885559757122,47.27675131192023,0.177829759194174,-0.06087656550203491,-0.07236527843171928,0.01148871292968437 2024-11-11 11:00:00+00:00,31.40999984741211,31.489999771118164,31.389999389648438,31.424999237060547,1129,5965.343029119318,31.444277396676643,46.83964075296742,0.17227051792814205,-0.05662820935960511,-0.06921786461729644,0.012589655257691332 2024-11-11 12:00:00+00:00,31.2549991607666,31.43000030517578,31.2450008392334,31.415000915527344,3360,5965.48088641827,31.426250898018544,40.57731889003265,0.17317972850058783,-0.06501911662304494,-0.06837811501844614,0.0033589983954012 2024-11-11 13:00:00+00:00,30.760000228881836,31.25,30.709999084472656,31.25,13393,5965.61874371722,31.362798453338858,27.796326886097418,0.19973832477154194,-0.11033932495275067,-0.07677035700530704,-0.03356896794744363 @@ -7562,7 +8494,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2025-01-10 07:00:00+00:00,31.094999313354492,31.155000686645508,31.024999618530273,31.145000457763672,816,6048.994861695215,31.044109625426437,58.27308546763755,0.11085495669256404,0.07365853306792758,0.07529097196291412,-0.0016324388949865432 2025-01-10 08:00:00+00:00,31.155000686645508,31.25,31.084999084472656,31.09000015258789,2107,6049.048149263773,31.054670678875873,62.5717118714583,0.11472252518076258,0.07361844343786572,0.07495646625790445,-0.0013380228200387256 2025-01-10 09:00:00+00:00,31.145000457763672,31.209999084472656,31.125,31.15999984741211,894,6049.10143683233,31.063273514960425,61.43573902726675,0.11259942227304069,0.07195033850706523,0.07435524070773661,-0.0024049022006713827 -2025-01-10 10:00:00+00:00,31.200000762939453,31.200000762939453,31.139999389648438,31.149999618530273,595,6049.154724400888,31.076295157625093,65.1799409233232,0.10884241877432461,0.07421096054388698,0.07432638467496669,-0.0001154241310797055 +2025-01-10 10:00:00+00:00,31.200000762939453,31.200000762939453,31.139999389648438,31.149999618530273,0,6049.154724400888,31.076295157625093,65.1799409233232,0.10884241877432461,0.07421096054388698,0.07432638467496669,-0.0001154241310797055 2025-01-10 11:00:00+00:00,31.21500015258789,31.28499984741211,31.184999465942383,31.200000762939453,1589,6049.208011969446,31.08950515714536,66.14529460220425,0.10821084468113905,0.07633292672082703,0.07472769308413876,0.0016052336366882691 2025-01-10 12:00:00+00:00,31.149999618530273,31.21500015258789,31.084999084472656,31.21500015258789,1782,6049.261299538003,31.095266534420116,58.56751807279251,0.10976728921214586,0.07194031714294269,0.07417021789589956,-0.0022299007529568704 2025-01-10 13:00:00+00:00,31.19499969482422,31.209999084472656,30.700000762939453,31.155000686645508,11063,6049.314587106561,31.104764930649075,61.82790984161887,0.13835522009222137,0.07126873091427655,0.07358992049957495,-0.0023211895852984082 @@ -8482,7 +9414,7 @@ Datetime,Close,High,Low,Open,Volume,SP500,EMA20,RSI14,ATR14,MACD,MACD_Signal,MAC 2025-03-11 06:00:00+00:00,32.650001525878906,32.65999984741211,32.27000045776367,32.529998779296875,1229,6125.675672849644,32.59270745941622,50.57761015562676,0.20279810775430468,-0.08348208927613854,-0.11522801749706671,0.03174592822092817 2025-03-11 07:00:00+00:00,32.65999984741211,32.67499923706055,32.595001220703125,32.650001525878906,1046,6125.7289604182015,32.59911625827297,51.07034436980422,0.19402667265452736,-0.06842002061340935,-0.10586641812033523,0.03744639750692588 2025-03-11 08:00:00+00:00,32.84000015258789,32.845001220703125,32.6349983215332,32.65999984741211,1855,6125.782247986759,32.62205758154106,58.99616562658108,0.19516783169134125,-0.04148054052900818,-0.09298924260206984,0.05150870207306166 -2025-03-11 09:00:00+00:00,32.94499969482422,32.96500015258789,32.84000015258789,32.84000015258789,1878,6125.835535555317,32.652813973282306,62.78328503465115,0.1901558437133883,-0.011525365691561262,-0.07669646721996813,0.06517110152840687 +2025-03-11 09:00:00+00:00,32.94499969482422,32.96500015258789,32.84000015258789,32.84000015258789,0,6125.835535555317,32.652813973282306,62.78328503465115,0.1901558437133883,-0.011525365691561262,-0.07669646721996813,0.06517110152840687 2025-03-11 10:00:00+00:00,32.9900016784668,33.02000045776367,32.91999816894531,32.94499969482422,1500,6125.888823123874,32.684927088061784,64.30495811985982,0.1837163040780291,0.01566503341116743,-0.05822416709374102,0.07388920050490845 2025-03-11 11:00:00+00:00,32.994998931884766,33.0099983215332,32.685001373291016,32.9900016784668,2052,6125.942110692432,32.71445773985445,64.47864111262787,0.1938077786611833,0.037188175050225425,-0.03914169866494773,0.07632987371517316 2025-03-11 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06:00:00+00:00,23.145000457763672,23.15999984741211,22.81999969482422,23.155000686645508,2508, +2023-09-13 07:00:00+00:00,23.235000610351562,23.235000610351562,23.110000610351562,23.145000457763672,1395, +2023-09-13 08:00:00+00:00,23.229999542236328,23.2549991607666,23.21500015258789,23.239999771118164,915, +2023-09-13 09:00:00+00:00,23.184999465942383,23.225000381469727,23.170000076293945,23.225000381469727,875, +2023-09-13 10:00:00+00:00,23.190000534057617,23.200000762939453,22.915000915527344,23.18000030517578,752, +2023-09-13 11:00:00+00:00,23.19499969482422,23.209999084472656,23.165000915527344,23.190000534057617,929, +2023-09-13 12:00:00+00:00,23.239999771118164,23.30500030517578,23.024999618530273,23.19499969482422,9381, +2023-09-13 13:00:00+00:00,23.209999084472656,23.334999084472656,23.110000610351562,23.2450008392334,10823, +2023-09-13 14:00:00+00:00,23.235000610351562,23.274999618530273,23.104999542236328,23.209999084472656,4497, +2023-09-13 15:00:00+00:00,23.170000076293945,23.270000457763672,23.139999389648438,23.229999542236328,2456, +2023-09-13 16:00:00+00:00,23.135000228881836,23.190000534057617,23.125,23.170000076293945,2199, +2023-09-13 17:00:00+00:00,23.149999618530273,23.19499969482422,23.104999542236328,23.135000228881836,2755, +2023-09-13 18:00:00+00:00,23.1200008392334,23.165000915527344,23.110000610351562,23.145000457763672,616, +2023-09-13 19:00:00+00:00,23.135000228881836,23.135000228881836,23.104999542236328,23.125,695, +2023-09-13 20:00:00+00:00,23.1299991607666,23.139999389648438,23.1200008392334,23.1299991607666,293, +2023-09-13 22:00:00+00:00,23.125,23.149999618530273,23.114999771118164,23.139999389648438,0, +2023-09-13 23:00:00+00:00,23.125,23.1299991607666,23.100000381469727,23.1200008392334,276, +2023-09-14 00:00:00+00:00,23.190000534057617,23.190000534057617,23.110000610351562,23.125,731, +2023-09-14 01:00:00+00:00,23.19499969482422,23.264999389648438,23.135000228881836,23.190000534057617,2892, +2023-09-14 02:00:00+00:00,23.049999237060547,23.200000762939453,23.045000076293945,23.184999465942383,2365, +2023-09-14 03:00:00+00:00,23.045000076293945,23.079999923706055,23.014999389648438,23.049999237060547,1538, +2023-09-14 04:00:00+00:00,23.0049991607666,23.040000915527344,23.0049991607666,23.040000915527344,364, +2023-09-14 05:00:00+00:00,22.915000915527344,23.114999771118164,22.895000457763672,23.0049991607666,3578, +2023-09-14 06:00:00+00:00,22.80500030517578,22.920000076293945,22.774999618530273,22.90999984741211,5017, +2023-09-14 07:00:00+00:00,22.764999389648438,22.815000534057617,22.739999771118164,22.809999465942383,2138, +2023-09-14 08:00:00+00:00,22.850000381469727,22.850000381469727,22.7549991607666,22.764999389648438,2148, +2023-09-14 09:00:00+00:00,22.81999969482422,22.854999542236328,22.799999237060547,22.844999313354492,1269, +2023-09-14 10:00:00+00:00,22.90999984741211,22.920000076293945,22.604999542236328,22.825000762939453,1644, +2023-09-14 11:00:00+00:00,22.84000015258789,22.924999237060547,22.834999084472656,22.915000915527344,1440, +2023-09-14 12:00:00+00:00,22.770000457763672,22.940000534057617,22.579999923706055,22.834999084472656,11684, +2023-09-14 13:00:00+00:00,22.704999923706055,22.885000228881836,22.670000076293945,22.764999389648438,12582, +2023-09-14 14:00:00+00:00,22.875,22.924999237060547,22.55500030517578,22.704999923706055,14431, +2023-09-14 15:00:00+00:00,22.84000015258789,22.979999542236328,22.770000457763672,22.8799991607666,7532, +2023-09-14 16:00:00+00:00,23.0,23.014999389648438,22.84000015258789,22.84000015258789,4040, +2023-09-14 17:00:00+00:00,22.915000915527344,23.020000457763672,22.90999984741211,23.0049991607666,2811, +2023-09-14 18:00:00+00:00,22.875,22.924999237060547,22.8700008392334,22.915000915527344,1107, +2023-09-14 19:00:00+00:00,22.889999389648438,22.895000457763672,22.854999542236328,22.8700008392334,1136, +2023-09-14 20:00:00+00:00,22.940000534057617,22.940000534057617,22.889999389648438,22.895000457763672,591, +2023-09-14 22:00:00+00:00,22.924999237060547,22.934999465942383,22.920000076293945,22.93000030517578,0, +2023-09-14 23:00:00+00:00,22.90999984741211,22.93000030517578,22.90999984741211,22.924999237060547,270, +2023-09-15 00:00:00+00:00,22.950000762939453,22.959999084472656,22.709999084472656,22.90999984741211,437, +2023-09-15 01:00:00+00:00,23.18000030517578,23.204999923706055,22.915000915527344,22.94499969482422,5048, +2023-09-15 02:00:00+00:00,23.15999984741211,23.239999771118164,23.125,23.184999465942383,1934, +2023-09-15 03:00:00+00:00,23.15999984741211,23.174999237060547,23.125,23.15999984741211,798, +2023-09-15 04:00:00+00:00,23.219999313354492,23.229999542236328,23.155000686645508,23.15999984741211,650, +2023-09-15 05:00:00+00:00,23.25,23.25,22.950000762939453,23.21500015258789,1136, +2023-09-15 06:00:00+00:00,23.3799991607666,23.440000534057617,23.2450008392334,23.25,6582, +2023-09-15 07:00:00+00:00,23.344999313354492,23.385000228881836,23.280000686645508,23.385000228881836,1734, +2023-09-15 08:00:00+00:00,23.350000381469727,23.375,23.295000076293945,23.344999313354492,1020, +2023-09-15 09:00:00+00:00,23.3799991607666,23.415000915527344,23.315000534057617,23.350000381469727,1655, +2023-09-15 10:00:00+00:00,23.354999542236328,23.424999237060547,23.354999542236328,23.3799991607666,960, +2023-09-15 11:00:00+00:00,23.450000762939453,23.469999313354492,23.334999084472656,23.360000610351562,3012, +2023-09-15 12:00:00+00:00,23.270000457763672,23.485000610351562,23.2450008392334,23.454999923706055,6457, +2023-09-15 13:00:00+00:00,23.3799991607666,23.575000762939453,23.229999542236328,23.270000457763672,11315, +2023-09-15 14:00:00+00:00,23.4950008392334,23.584999084472656,23.184999465942383,23.385000228881836,8908, +2023-09-15 15:00:00+00:00,23.459999084472656,23.540000915527344,23.395000457763672,23.4950008392334,4555, +2023-09-15 16:00:00+00:00,23.395000457763672,23.5,23.389999389648438,23.459999084472656,2470, +2023-09-15 17:00:00+00:00,23.350000381469727,23.399999618530273,23.34000015258789,23.399999618530273,3351, +2023-09-15 18:00:00+00:00,23.344999313354492,23.3700008392334,23.34000015258789,23.354999542236328,943, +2023-09-15 19:00:00+00:00,23.299999237060547,23.360000610351562,23.290000915527344,23.354999542236328,1418, +2023-09-15 20:00:00+00:00,23.30500030517578,23.315000534057617,23.260000228881836,23.295000076293945,808, 2023-09-17 22:00:00+00:00,23.325000762939453,23.34000015258789,23.309999465942383,23.31999969482422,237, 2023-09-17 23:00:00+00:00,23.334999084472656,23.334999084472656,23.309999465942383,23.325000762939453,295, 2023-09-18 00:00:00+00:00,23.350000381469727,23.354999542236328,23.315000534057617,23.329999923706055,471, @@ -10,7 +942,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2023-09-18 06:00:00+00:00,23.395000457763672,23.5049991607666,23.360000610351562,23.485000610351562,3325, 2023-09-18 07:00:00+00:00,23.40999984741211,23.44499969482422,23.395000457763672,23.395000457763672,1044, 2023-09-18 08:00:00+00:00,23.395000457763672,23.420000076293945,23.360000610351562,23.405000686645508,593, -2023-09-18 09:00:00+00:00,23.43000030517578,23.434999465942383,23.385000228881836,23.395000457763672,589, +2023-09-18 09:00:00+00:00,23.43000030517578,23.434999465942383,23.385000228881836,23.395000457763672,0, 2023-09-18 10:00:00+00:00,23.475000381469727,23.485000610351562,23.424999237060547,23.43000030517578,1019, 2023-09-18 11:00:00+00:00,23.420000076293945,23.5,23.405000686645508,23.469999313354492,1495, 2023-09-18 12:00:00+00:00,23.34000015258789,23.44499969482422,23.325000762939453,23.420000076293945,3481, @@ -965,7 +1897,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2023-11-14 19:00:00+00:00,23.15999984741211,23.170000076293945,23.1299991607666,23.155000686645508,1541, 2023-11-14 20:00:00+00:00,23.174999237060547,23.200000762939453,23.15999984741211,23.15999984741211,1190, 2023-11-14 21:00:00+00:00,23.149999618530273,23.174999237060547,23.145000457763672,23.174999237060547,0, -2023-11-14 23:00:00+00:00,23.125,23.190000534057617,23.1200008392334,23.15999984741211,0, +2023-11-14 23:00:00+00:00,23.125,23.190000534057617,23.1200008392334,23.15999984741211,1334, 2023-11-15 00:00:00+00:00,23.165000915527344,23.165000915527344,23.110000610351562,23.125,569, 2023-11-15 01:00:00+00:00,23.145000457763672,23.18000030517578,23.094999313354492,23.165000915527344,1626, 2023-11-15 02:00:00+00:00,23.184999465942383,23.19499969482422,23.125,23.149999618530273,831, @@ -1022,7 +1954,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2023-11-17 07:00:00+00:00,23.934999465942383,23.934999465942383,23.80500030517578,23.844999313354492,1616, 2023-11-17 08:00:00+00:00,24.020000457763672,24.020000457763672,23.90999984741211,23.934999465942383,1437, 2023-11-17 09:00:00+00:00,24.094999313354492,24.125,24.0049991607666,24.010000228881836,2588, -2023-11-17 10:00:00+00:00,24.21500015258789,24.219999313354492,24.079999923706055,24.100000381469727,2719, +2023-11-17 10:00:00+00:00,24.21500015258789,24.219999313354492,24.079999923706055,24.100000381469727,0, 2023-11-17 11:00:00+00:00,24.075000762939453,24.21500015258789,24.03499984741211,24.21500015258789,2499, 2023-11-17 12:00:00+00:00,24.110000610351562,24.149999618530273,24.05500030517578,24.075000762939453,1343, 2023-11-17 13:00:00+00:00,23.975000381469727,24.165000915527344,23.940000534057617,24.114999771118164,7605, @@ -1892,7 +2824,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2024-01-16 07:00:00+00:00,23.295000076293945,23.329999923706055,23.2450008392334,23.295000076293945,655, 2024-01-16 08:00:00+00:00,23.229999542236328,23.375,23.21500015258789,23.290000915527344,1454, 2024-01-16 09:00:00+00:00,23.2450008392334,23.25,23.15999984741211,23.229999542236328,1497, -2024-01-16 10:00:00+00:00,23.174999237060547,23.280000686645508,23.170000076293945,23.25,857, +2024-01-16 10:00:00+00:00,23.174999237060547,23.280000686645508,23.170000076293945,23.25,0, 2024-01-16 11:00:00+00:00,23.204999923706055,23.21500015258789,23.149999618530273,23.174999237060547,772, 2024-01-16 12:00:00+00:00,23.204999923706055,23.235000610351562,23.1299991607666,23.204999923706055,1258, 2024-01-16 13:00:00+00:00,23.155000686645508,23.334999084472656,23.084999084472656,23.204999923706055,7297, @@ -2825,7 +3757,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2024-03-13 19:00:00+00:00,25.145000457763672,25.225000381469727,25.135000228881836,25.209999084472656,2536, 2024-03-13 20:00:00+00:00,25.225000381469727,25.25,25.139999389648438,25.145000457763672,2023, 2024-03-13 22:00:00+00:00,25.239999771118164,25.260000228881836,25.19499969482422,25.225000381469727,0, -2024-03-13 23:00:00+00:00,25.2549991607666,25.260000228881836,25.219999313354492,25.2450008392334,0, +2024-03-13 23:00:00+00:00,25.2549991607666,25.260000228881836,25.219999313354492,25.2450008392334,987, 2024-03-14 00:00:00+00:00,25.270000457763672,25.295000076293945,25.225000381469727,25.260000228881836,1924, 2024-03-14 01:00:00+00:00,25.309999465942383,25.3700008392334,25.235000610351562,25.274999618530273,3953, 2024-03-14 02:00:00+00:00,25.229999542236328,25.315000534057617,25.219999313354492,25.309999465942383,1913, @@ -3768,7 +4700,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2024-05-10 19:00:00+00:00,28.440000534057617,28.4950008392334,28.424999237060547,28.475000381469727,1228, 2024-05-10 20:00:00+00:00,28.395000457763672,28.44499969482422,28.385000228881836,28.434999465942383,790, 2024-05-12 22:00:00+00:00,28.360000610351562,28.450000762939453,28.344999313354492,28.420000076293945,673, -2024-05-12 23:00:00+00:00,28.395000457763672,28.440000534057617,28.360000610351562,28.360000610351562,0, +2024-05-12 23:00:00+00:00,28.395000457763672,28.440000534057617,28.360000610351562,28.360000610351562,644, 2024-05-13 00:00:00+00:00,28.454999923706055,28.459999084472656,28.399999618530273,28.399999618530273,531, 2024-05-13 01:00:00+00:00,28.350000381469727,28.454999923706055,28.184999465942383,28.454999923706055,3592, 2024-05-13 02:00:00+00:00,28.389999389648438,28.399999618530273,28.315000534057617,28.344999313354492,1084, @@ -3824,7 +4756,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2024-05-15 06:00:00+00:00,28.875,28.875,28.770000457763672,28.850000381469727,1249, 2024-05-15 07:00:00+00:00,28.895000457763672,28.940000534057617,28.875,28.875,3106, 2024-05-15 08:00:00+00:00,29.15999984741211,29.15999984741211,28.899999618530273,28.899999618530273,5126, -2024-05-15 09:00:00+00:00,29.084999084472656,29.18000030517578,29.065000534057617,29.15999984741211,3127, +2024-05-15 09:00:00+00:00,29.084999084472656,29.18000030517578,29.065000534057617,29.15999984741211,0, 2024-05-15 10:00:00+00:00,29.065000534057617,29.170000076293945,29.03499984741211,29.09000015258789,2202, 2024-05-15 11:00:00+00:00,29.024999618530273,29.100000381469727,29.0049991607666,29.065000534057617,2418, 2024-05-15 12:00:00+00:00,29.334999084472656,29.440000534057617,28.93000030517578,29.030000686645508,16066, @@ -4691,7 +5623,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2024-07-11 18:00:00+00:00,31.725000381469727,31.735000610351562,31.65999984741211,31.690000534057617,1167, 2024-07-11 19:00:00+00:00,31.69499969482422,31.774999618530273,31.69499969482422,31.719999313354492,1542, 2024-07-11 20:00:00+00:00,31.725000381469727,31.725000381469727,31.655000686645508,31.690000534057617,0, -2024-07-11 22:00:00+00:00,31.649999618530273,31.725000381469727,31.610000610351562,31.725000381469727,0, +2024-07-11 22:00:00+00:00,31.649999618530273,31.725000381469727,31.610000610351562,31.725000381469727,470, 2024-07-11 23:00:00+00:00,31.65999984741211,31.670000076293945,31.610000610351562,31.645000457763672,438, 2024-07-12 00:00:00+00:00,31.579999923706055,31.65999984741211,31.549999237060547,31.655000686645508,1022, 2024-07-12 01:00:00+00:00,31.479999542236328,31.635000228881836,31.405000686645508,31.579999923706055,2780, @@ -5635,7 +6567,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2024-09-09 19:00:00+00:00,28.670000076293945,28.674999237060547,28.6299991607666,28.655000686645508,858, 2024-09-09 20:00:00+00:00,28.69499969482422,28.709999084472656,28.670000076293945,28.674999237060547,508, 2024-09-09 22:00:00+00:00,28.704999923706055,28.709999084472656,28.68000030517578,28.690000534057617,0, -2024-09-09 23:00:00+00:00,28.690000534057617,28.700000762939453,28.68000030517578,28.69499969482422,0, +2024-09-09 23:00:00+00:00,28.690000534057617,28.700000762939453,28.68000030517578,28.69499969482422,145, 2024-09-10 00:00:00+00:00,28.665000915527344,28.690000534057617,28.6200008392334,28.690000534057617,606, 2024-09-10 01:00:00+00:00,28.635000228881836,28.7549991607666,28.614999771118164,28.670000076293945,2272, 2024-09-10 02:00:00+00:00,28.639999389648438,28.655000686645508,28.575000762939453,28.639999389648438,1076, @@ -5691,7 +6623,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2024-09-12 06:00:00+00:00,29.049999237060547,29.149999618530273,29.040000915527344,29.1200008392334,1210, 2024-09-12 07:00:00+00:00,29.165000915527344,29.209999084472656,28.80500030517578,29.05500030517578,2117, 2024-09-12 08:00:00+00:00,29.1299991607666,29.2450008392334,28.834999084472656,29.165000915527344,1621, -2024-09-12 09:00:00+00:00,29.1299991607666,29.155000686645508,28.790000915527344,29.1299991607666,553, +2024-09-12 09:00:00+00:00,29.1299991607666,29.155000686645508,28.790000915527344,29.1299991607666,0, 2024-09-12 10:00:00+00:00,29.075000762939453,29.174999237060547,29.075000762939453,29.125,582, 2024-09-12 11:00:00+00:00,29.125,29.145000457763672,29.03499984741211,29.079999923706055,1172, 2024-09-12 12:00:00+00:00,29.395000457763672,29.5,29.075000762939453,29.125,8771, @@ -6657,7 +7589,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2024-11-11 07:00:00+00:00,31.299999237060547,31.5,31.274999618530273,31.475000381469727,1229, 2024-11-11 08:00:00+00:00,31.399999618530273,31.46500015258789,31.299999237060547,31.30500030517578,1484, 2024-11-11 09:00:00+00:00,31.450000762939453,31.459999084472656,31.344999313354492,31.405000686645508,1346, -2024-11-11 10:00:00+00:00,31.420000076293945,31.65999984741211,31.405000686645508,31.450000762939453,2978, +2024-11-11 10:00:00+00:00,31.420000076293945,31.65999984741211,31.405000686645508,31.450000762939453,0, 2024-11-11 11:00:00+00:00,31.40999984741211,31.489999771118164,31.389999389648438,31.424999237060547,1129, 2024-11-11 12:00:00+00:00,31.2549991607666,31.43000030517578,31.2450008392334,31.415000915527344,3360, 2024-11-11 13:00:00+00:00,30.760000228881836,31.25,30.709999084472656,31.25,13393, @@ -7562,7 +8494,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2025-01-10 07:00:00+00:00,31.094999313354492,31.155000686645508,31.024999618530273,31.145000457763672,816, 2025-01-10 08:00:00+00:00,31.155000686645508,31.25,31.084999084472656,31.09000015258789,2107, 2025-01-10 09:00:00+00:00,31.145000457763672,31.209999084472656,31.125,31.15999984741211,894, -2025-01-10 10:00:00+00:00,31.200000762939453,31.200000762939453,31.139999389648438,31.149999618530273,595, +2025-01-10 10:00:00+00:00,31.200000762939453,31.200000762939453,31.139999389648438,31.149999618530273,0, 2025-01-10 11:00:00+00:00,31.21500015258789,31.28499984741211,31.184999465942383,31.200000762939453,1589, 2025-01-10 12:00:00+00:00,31.149999618530273,31.21500015258789,31.084999084472656,31.21500015258789,1782, 2025-01-10 13:00:00+00:00,31.19499969482422,31.209999084472656,30.700000762939453,31.155000686645508,11063, @@ -8482,7 +9414,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2025-03-11 06:00:00+00:00,32.650001525878906,32.65999984741211,32.27000045776367,32.529998779296875,1229, 2025-03-11 07:00:00+00:00,32.65999984741211,32.67499923706055,32.595001220703125,32.650001525878906,1046, 2025-03-11 08:00:00+00:00,32.84000015258789,32.845001220703125,32.6349983215332,32.65999984741211,1855, -2025-03-11 09:00:00+00:00,32.94499969482422,32.96500015258789,32.84000015258789,32.84000015258789,1878, +2025-03-11 09:00:00+00:00,32.94499969482422,32.96500015258789,32.84000015258789,32.84000015258789,0, 2025-03-11 10:00:00+00:00,32.9900016784668,33.02000045776367,32.91999816894531,32.94499969482422,1500, 2025-03-11 11:00:00+00:00,32.994998931884766,33.0099983215332,32.685001373291016,32.9900016784668,2052, 2025-03-11 12:00:00+00:00,32.96500015258789,33.0099983215332,32.689998626708984,32.994998931884766,3371, @@ -10353,7 +11285,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2025-07-04 14:00:00+00:00,37.09000015258789,37.11000061035156,37.029998779296875,37.099998474121094,684, 2025-07-04 15:00:00+00:00,37.119998931884766,37.125,37.06999969482422,37.09000015258789,379, 2025-07-04 16:00:00+00:00,37.1349983215332,37.154998779296875,37.10499954223633,37.1150016784668,595, -2025-07-06 22:00:00+00:00,37.16999816894531,37.16999816894531,37.125,37.150001525878906,0, +2025-07-06 22:00:00+00:00,37.33000183105469,37.435001373291016,37.09000015258789,37.150001525878906,1802, 2025-07-06 23:00:00+00:00,37.23500061035156,37.334999084472656,37.209999084472656,37.33000183105469,907, 2025-07-07 00:00:00+00:00,37.21500015258789,37.244998931884766,37.14500045776367,37.2400016784668,883, 2025-07-07 01:00:00+00:00,37.10499954223633,37.209999084472656,36.88999938964844,37.209999084472656,3265, @@ -10410,7 +11342,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2025-07-09 06:00:00+00:00,36.790000915527344,36.90999984741211,36.720001220703125,36.89500045776367,1584, 2025-07-09 07:00:00+00:00,36.72999954223633,36.80500030517578,36.68000030517578,36.79999923706055,1566, 2025-07-09 08:00:00+00:00,36.689998626708984,36.78499984741211,36.619998931884766,36.72999954223633,1297, -2025-07-09 09:00:00+00:00,36.810001373291016,36.810001373291016,36.61000061035156,36.689998626708984,1241, +2025-07-09 09:00:00+00:00,36.810001373291016,36.810001373291016,36.61000061035156,36.689998626708984,0, 2025-07-09 10:00:00+00:00,36.79999923706055,36.85499954223633,36.7599983215332,36.810001373291016,821, 2025-07-09 11:00:00+00:00,36.70000076293945,36.8650016784668,36.69499969482422,36.80500030517578,1719, 2025-07-09 12:00:00+00:00,36.625,36.73500061035156,36.3849983215332,36.70500183105469,4376, @@ -10514,7 +11446,7 @@ Datetime,Close,High,Low,Open,Volume,SP500 2025-07-16 01:00:00+00:00,38.10499954223633,38.14500045776367,38.060001373291016,38.099998474121094,1533, 2025-07-16 02:00:00+00:00,38.060001373291016,38.1150016784668,37.994998931884766,38.11000061035156,1055, 2025-07-16 03:00:00+00:00,38.1349983215332,38.154998779296875,38.060001373291016,38.060001373291016,843, -2025-07-16 04:00:00+00:00,38.154998779296875,38.15999984741211,38.11000061035156,38.13999938964844,488, +2025-07-16 04:00:00+00:00,38.154998779296875,38.15999984741211,38.11000061035156,38.13999938964844,491, 2025-07-16 05:00:00+00:00,38.1349983215332,38.15999984741211,38.095001220703125,38.15999984741211,917, 2025-07-16 06:00:00+00:00,38.185001373291016,38.22999954223633,38.119998931884766,38.130001068115234,1901, 2025-07-16 07:00:00+00:00,38.23500061035156,38.25,38.150001525878906,38.185001373291016,1339, @@ -10525,4 +11457,55 @@ Datetime,Close,High,Low,Open,Volume,SP500 2025-07-16 12:00:00+00:00,38.11000061035156,38.29999923706055,38.099998474121094,38.2599983215332,3072, 2025-07-16 13:00:00+00:00,37.95000076293945,38.22999954223633,37.845001220703125,38.1150016784668,5730, 2025-07-16 14:00:00+00:00,38.025001525878906,38.04999923706055,37.75,37.94499969482422,7234, -2025-07-16 15:00:00+00:00,38.22999954223633,38.380001068115234,37.994998931884766,38.025001525878906,10980, +2025-07-16 15:00:00+00:00,38.060001373291016,38.380001068115234,37.959999084472656,38.025001525878906,14880, +2025-07-16 16:00:00+00:00,38.03499984741211,38.17499923706055,37.96500015258789,38.05500030517578,3259, +2025-07-16 17:00:00+00:00,38.15999984741211,38.19499969482422,38.03499984741211,38.03499984741211,2448, +2025-07-16 18:00:00+00:00,38.1150016784668,38.165000915527344,38.095001220703125,38.165000915527344,1239, +2025-07-16 19:00:00+00:00,38.099998474121094,38.130001068115234,38.07500076293945,38.11000061035156,1058, +2025-07-16 20:00:00+00:00,38.125,38.15999984741211,38.099998474121094,38.099998474121094,534, +2025-07-16 22:00:00+00:00,38.13999938964844,38.185001373291016,38.125,38.14500045776367,0, +2025-07-16 23:00:00+00:00,38.11000061035156,38.150001525878906,38.099998474121094,38.13999938964844,216, +2025-07-17 00:00:00+00:00,38.07500076293945,38.130001068115234,38.05500030517578,38.119998931884766,475, +2025-07-17 01:00:00+00:00,38.20500183105469,38.21500015258789,38.060001373291016,38.064998626708984,1028, +2025-07-17 02:00:00+00:00,38.18000030517578,38.224998474121094,38.084999084472656,38.20000076293945,906, +2025-07-17 03:00:00+00:00,38.25,38.255001068115234,38.13999938964844,38.18000030517578,623, +2025-07-17 04:00:00+00:00,38.26499938964844,38.275001525878906,38.2400016784668,38.244998931884766,436, +2025-07-17 05:00:00+00:00,38.25,38.28499984741211,38.19499969482422,38.2599983215332,896, +2025-07-17 06:00:00+00:00,38.125,38.275001525878906,38.029998779296875,38.255001068115234,2796, +2025-07-17 07:00:00+00:00,37.9900016784668,38.165000915527344,37.970001220703125,38.125,2317, +2025-07-17 08:00:00+00:00,37.95000076293945,38.03499984741211,37.935001373291016,38.0,1078, +2025-07-17 09:00:00+00:00,37.98500061035156,38.029998779296875,37.935001373291016,37.95500183105469,801, +2025-07-17 10:00:00+00:00,37.95500183105469,38.06999969482422,37.95500183105469,37.9900016784668,648, +2025-07-17 11:00:00+00:00,38.06999969482422,38.095001220703125,37.94499969482422,37.95500183105469,1038, +2025-07-17 12:00:00+00:00,37.78499984741211,38.125,37.685001373291016,38.06999969482422,6264, +2025-07-17 13:00:00+00:00,37.95000076293945,38.0099983215332,37.744998931884766,37.79499816894531,6560, +2025-07-17 14:00:00+00:00,38.185001373291016,38.20500183105469,37.93000030517578,37.95500183105469,4491, +2025-07-17 15:00:00+00:00,38.27000045776367,38.334999084472656,38.150001525878906,38.189998626708984,4743, +2025-07-17 16:00:00+00:00,38.244998931884766,38.30500030517578,38.220001220703125,38.27000045776367,1831, +2025-07-17 17:00:00+00:00,38.35499954223633,38.36000061035156,38.2400016784668,38.244998931884766,2830, +2025-07-17 18:00:00+00:00,38.400001525878906,38.41999816894531,38.32500076293945,38.35499954223633,1637, +2025-07-17 19:00:00+00:00,38.45000076293945,38.459999084472656,38.400001525878906,38.400001525878906,1540, +2025-07-17 20:00:00+00:00,38.435001373291016,38.45500183105469,38.380001068115234,38.45000076293945,787, +2025-07-17 22:00:00+00:00,38.435001373291016,38.48500061035156,38.404998779296875,38.42499923706055,0, +2025-07-17 23:00:00+00:00,38.41999816894531,38.44499969482422,38.404998779296875,38.435001373291016,193, +2025-07-18 00:00:00+00:00,38.46500015258789,38.470001220703125,38.40999984741211,38.41999816894531,689, +2025-07-18 01:00:00+00:00,38.474998474121094,38.55500030517578,38.38999938964844,38.46500015258789,2670, +2025-07-18 02:00:00+00:00,38.54999923706055,38.58000183105469,38.435001373291016,38.474998474121094,1413, +2025-07-18 03:00:00+00:00,38.494998931884766,38.564998626708984,38.45500183105469,38.55500030517578,880, +2025-07-18 04:00:00+00:00,38.45500183105469,38.5,38.45500183105469,38.47999954223633,441, +2025-07-18 05:00:00+00:00,38.40999984741211,38.5099983215332,38.40999984741211,38.45500183105469,768, +2025-07-18 06:00:00+00:00,38.720001220703125,38.744998931884766,38.375,38.40999984741211,3117, +2025-07-18 07:00:00+00:00,38.70500183105469,38.82500076293945,38.60499954223633,38.71500015258789,2670, +2025-07-18 08:00:00+00:00,38.64500045776367,38.720001220703125,38.6349983215332,38.70500183105469,913, +2025-07-18 09:00:00+00:00,38.744998931884766,38.810001373291016,38.64500045776367,38.650001525878906,1346, +2025-07-18 10:00:00+00:00,38.654998779296875,38.755001068115234,38.630001068115234,38.7400016784668,953, +2025-07-18 11:00:00+00:00,38.73500061035156,38.77000045776367,38.6150016784668,38.650001525878906,1983, +2025-07-18 12:00:00+00:00,38.76499938964844,38.80500030517578,38.685001373291016,38.73500061035156,2418, +2025-07-18 13:00:00+00:00,38.57500076293945,38.77000045776367,38.5099983215332,38.76499938964844,5829, +2025-07-18 14:00:00+00:00,38.58000183105469,38.68000030517578,38.53499984741211,38.57500076293945,2831, +2025-07-18 15:00:00+00:00,38.529998779296875,38.61000061035156,38.44499969482422,38.58000183105469,3503, +2025-07-18 16:00:00+00:00,38.474998474121094,38.57500076293945,38.45500183105469,38.525001525878906,1908, +2025-07-18 17:00:00+00:00,38.53499984741211,38.53499984741211,38.404998779296875,38.474998474121094,2673, +2025-07-18 18:00:00+00:00,38.4900016784668,38.55500030517578,38.470001220703125,38.53499984741211,1635, +2025-07-18 19:00:00+00:00,38.3849983215332,38.5,38.375,38.4900016784668,1059, +2025-07-18 20:00:00+00:00,38.42499923706055,38.470001220703125,38.3849983215332,38.38999938964844,1042, diff --git a/data/silver_xgb_model/Close_predictor.joblib b/data/silver_xgb_model/Close_predictor.joblib index 5adef2c..2d2802f 100644 Binary files a/data/silver_xgb_model/Close_predictor.joblib and b/data/silver_xgb_model/Close_predictor.joblib differ diff --git a/data/silver_xgb_model/High_predictor.joblib b/data/silver_xgb_model/High_predictor.joblib index 0f40201..3914280 100644 Binary files a/data/silver_xgb_model/High_predictor.joblib and b/data/silver_xgb_model/High_predictor.joblib differ diff --git a/data/silver_xgb_model/Low_predictor.joblib b/data/silver_xgb_model/Low_predictor.joblib index ec5541e..2b0d4b4 100644 Binary files a/data/silver_xgb_model/Low_predictor.joblib and b/data/silver_xgb_model/Low_predictor.joblib differ diff --git a/data/silver_xgb_model/Volume_predictor.joblib b/data/silver_xgb_model/Volume_predictor.joblib index a25e1d5..8f4cbfd 100644 Binary files a/data/silver_xgb_model/Volume_predictor.joblib and b/data/silver_xgb_model/Volume_predictor.joblib differ diff --git a/data/update_model.py b/data/update_model.py index 208f350..a1a5694 100644 --- a/data/update_model.py +++ b/data/update_model.py @@ -1,18 +1,44 @@ from forecasting_framework import ForecastFramework -from forecasting_models import ClosePriceFM +from forecasting_models import ClosePriceFM, ClosePriceFM_Silver +from data_loader import GoldDataLoader, SilverDataLoader + + +model_params = [ + { + 'data_loader': GoldDataLoader(), + 'target_columns': ['Close'], + 'forecast_model': ClosePriceFM(), + 'name': 'xgb_model' + }, + { + 'data_loader': SilverDataLoader(), + 'target_columns': ['Close'], + 'forecast_model': ClosePriceFM_Silver(), + 'name': 'silver_xgb_model' + } +] def update_model(): - # Create a new framework object - fm = ForecastFramework( - target_columns=['Close'], - forecast_model=ClosePriceFM(), - name='xgb_model' - ) - - # Train model - fm.train_model() - - # Dump model - path: str = "xgb_model" - fm.dump_model(path=path) + for params in model_params: + # Create a new framework object + fm = ForecastFramework( + data_loader=params['data_loader'], + target_columns=params['target_columns'], + forecast_model=params['forecast_model'], + name=params['name'] + ) + + # Train model + try: + fm.train_model() + except Exception as e: + print(f"Training failed for {params['name']}: {e}") + + # Dump model + fm.dump_model(path=params['name']) + print(f"✅ Model '{params['name']}' saved to: {params['name']}/") + + +if __name__ == "__main__": + update_model() diff --git a/docker-compose.yml b/docker-compose.yml index 8b8c1ae..a1f258b 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,5 +1,3 @@ -version: '3.9' - services: db: image: postgres:13 @@ -7,7 +5,7 @@ services: - .env # File containing only DB credentials environment: POSTGRES_DB: metalytics - POSTGRES_USER: ${DB_USER:-postgres} + POSTGRES_USER: ${DB_USER:-admin} POSTGRES_PASSWORD: ${DB_PASSWORD} volumes: - postgres_data:/var/lib/postgresql/data @@ -17,7 +15,7 @@ services: networks: - metalytics_net healthcheck: - test: ["CMD-SHELL", "pg_isready -U postgres"] + test: ["CMD-SHELL", "pg_isready -U admin -d metalytics"] interval: 5s timeout: 5s retries: 5 @@ -35,7 +33,7 @@ services: DB_HOST: db DB_PORT: 5432 DB_NAME: metalytics - DB_USER: ${DB_USER:-postgres} + DB_USER: ${DB_USER:-admin} DB_PASSWORD: ${DB_PASSWORD} frontend: @@ -63,7 +61,7 @@ services: DB_HOST: db DB_PORT: 5432 DB_NAME: metalytics - DB_USER: ${DB_USER:-postgres} + DB_USER: ${DB_USER:-admin} DB_PASSWORD: ${DB_PASSWORD} networks: diff --git a/ml/zinc_macro_features.csv b/ml/zinc_macro_features.csv new file mode 100644 index 0000000..57c66ab --- /dev/null +++ b/ml/zinc_macro_features.csv @@ -0,0 +1,2610 @@ +Date,Zn_Price,DXY,Brent_USD,Zinc_Production,Global_PMI +2015-07-17,2087.50000,97.86000,57.52000,113.49310, +2015-07-20,2075.00000,98.03000,56.65000,113.49310, +2015-07-21,2059.00000,97.33000,57.04000,113.49310, +2015-07-22,2026.00000,97.60000,56.13000,113.49310, +2015-07-23,2006.00000,97.12000,55.27000,113.49310, +2015-07-24,2000.00000,97.24000,54.62000,113.49310, +2015-07-27,1956.50000,96.50000,53.47000,113.49310, +2015-07-28,2000.00000,96.77000,53.30000,113.49310, +2015-07-29,1990.00000,96.98000,53.38000,113.49310, 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series from Yahoo Finance.""" + try: + df = yf.download( + ticker, period="10y", interval="1d", progress=False, auto_adjust=False + ) + + if df is None or df.empty: + logging.warning( + "yfinance %s (%s) returned an empty or None DataFrame.", ticker, name + ) + return pd.Series(name=name, dtype="float64") + + close_series = None + # yfinance sometimes returns multi-level columns, so we handle both cases. + if "Close" in df.columns: + s = df["Close"] + if isinstance(s, pd.DataFrame): + # Multi-level columns: the result is a DataFrame with one column. + if not s.empty: + close_series = s.iloc[:, 0] + elif isinstance(s, pd.Series): + # Single-level columns: we get a Series directly. + close_series = s + + if close_series is not None: + result = close_series.copy() + result.name = name + return result + + logging.warning("Could not extract 'Close' series for %s (%s).", ticker, name) + logging.info("DataFrame columns for %s (%s): %s", ticker, name, df.columns) + return pd.Series(name=name, dtype="float64") + + except Exception as e: + logging.error("yfinance %s (%s) failed with exception: %s", ticker, name, e) + return pd.Series(name=name, dtype="float64") + + +# ---------- helper: FRED data ------------------------------------ +def fred_csv(series_id: str, name: str) -> pd.Series: + """Fetch a series from FRED as a CSV.""" + logging.info("Fetching %s from FRED (%s)", name, series_id) + url = f"https://fred.stlouisfed.org/graph/fredgraph.csv?id={series_id}" + try: + r = requests.get(url, timeout=18) + if r.status_code != 200: + logging.debug("FRED CSV %s HTTP %s", series_id, r.status_code) + return pd.Series(name=name, dtype="float64") + + csv_data = StringIO(r.text) + # The date column name can vary ('DATE' or 'observation_date'), + # so we use the first column as the index to be more robust. + df = pd.read_csv(csv_data, index_col=0, parse_dates=True) + + s = df[series_id].copy() + if isinstance(s, pd.Series): + s.name = name + s.replace(".", pd.NA, inplace=True) + s = s.ffill() + s = s.astype(float) + return s + + logging.warning("FRED data for %s is not a Series.", name) + return pd.Series(name=name, dtype="float64") + + except ValueError as e: + logging.error("Failed to parse FRED CSV for %s due to ValueError: %s", name, e) + if "r" in locals() and r is not None: + logging.error("Response text from FRED that caused the error: %s", r.text) + return pd.Series(name=name, dtype="float64") + except Exception as e: + logging.error( + "Failed to parse FRED CSV for %s with a general exception: %s", + name, + e, + exc_info=True, + ) + return pd.Series(name=name, dtype="float64") + + +# ---------- Data Fetching --------------------------------------- + +# Financial data from Yahoo Finance +zn_price = yf_close("ZNC=F", "Zn_Price") +dxy = yf_close("DX-Y.NYB", "DXY") +brent = yf_close("BZ=F", "Brent_USD") + +# Macroeconomic data from FRED +# The previous PMI series was unreliable; switching to the ISM series. +global_pmi = fred_csv("ISMIR", "Global_PMI") +zinc_production = fred_csv("IPG21223SQ", "Zinc_Production") + + +# ---------- Assemble Dataframe ---------------------------------- +all_series = [ + zn_price, + dxy, + brent, + zinc_production, + global_pmi, +] +df = pd.concat(all_series, axis=1).sort_index() + +if df.empty: + logging.error("No data collected! Check network or proxies.") +else: + # Filter for the last 10 years to align all data series + ten_years_ago = pd.to_datetime("today") - pd.DateOffset(years=10) + df = df[df.index >= ten_years_ago] + + df = df.asfreq("B") + + # Backward-fill the scraped single-point values + scraped_cols = ["Zinc_Production", "Global_PMI"] + for col in scraped_cols: + if col in df.columns: + series = df[col] + if isinstance(series, pd.Series): + df[col] = series.bfill().ffill() # bfill then ffill for good measure + + logging.info("DataFrame head after filling:\n%s", df.head().to_string()) + logging.info("DataFrame tail after filling:\n%s", df.tail().to_string()) + + # Interpolate the financial time series + df.interpolate(method="time", inplace=True) + + df.to_csv("zinc_macro_features.csv", index_label="Date", float_format="%.5f") + logging.info( + "Saved %d rows × %d cols → zinc_macro_features.csv", + len(df), + len(df.columns), + )