diff --git a/backend/requirements.txt b/backend/requirements.txt index 135d2d9..88b66b7 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -6,4 +6,7 @@ psutil uvicorn pytest flake8 +tensorflow +beautifulsoup4==4.12.3 +quandl==3.7.0 httpx diff --git a/ml/forecasting_models.py b/ml/forecasting_models.py index f3c5b1b..8219b92 100644 --- a/ml/forecasting_models.py +++ b/ml/forecasting_models.py @@ -994,7 +994,7 @@ 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, @@ -1013,13 +1013,14 @@ def __init__( def build(self, df: pd.DataFrame): 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() - if 'SP500' in df.columns: - df['SP500'] = pd.to_numeric(df['SP500'], errors='coerce').interpolate(method='time') + #if 'SP500' in df.columns: + # df['SP500'] = pd.to_numeric(df['SP500'], errors='coerce').interpolate(method='time') df = df.dropna() @@ -1037,7 +1038,8 @@ 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) @@ -1051,11 +1053,12 @@ def build_forecast_data(self, df: pd.DataFrame) -> pd.DataFrame: # Set Indicators for indicator in self.indicators: df.loc[:, indicator] = np.nan - if indicator == 'SP500': + #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], '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 @@ -1148,51 +1151,76 @@ def fit( return self 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 = [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) - y_ = self.model_.predict(x_) - + 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])]) - - # 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: 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] - + 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')] - - # 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) + y_pred = pd.Series(y_pred, index=date_range) + price_prediction = initial_price + np.cumsum(y_pred) return price_prediction def dump(self, path: str) -> None: diff --git a/ml/silver_xgb_model/Close_predictor.joblib b/ml/silver_xgb_model/Close_predictor.joblib index 5adef2c..2d2802f 100644 Binary files a/ml/silver_xgb_model/Close_predictor.joblib and b/ml/silver_xgb_model/Close_predictor.joblib differ diff --git a/ml/silver_xgb_model/High_predictor.joblib b/ml/silver_xgb_model/High_predictor.joblib index 0f40201..3914280 100644 Binary files a/ml/silver_xgb_model/High_predictor.joblib and b/ml/silver_xgb_model/High_predictor.joblib differ diff --git a/ml/silver_xgb_model/Low_predictor.joblib b/ml/silver_xgb_model/Low_predictor.joblib index ec5541e..2b0d4b4 100644 Binary files a/ml/silver_xgb_model/Low_predictor.joblib and b/ml/silver_xgb_model/Low_predictor.joblib differ diff --git a/ml/silver_xgb_model/Volume_predictor.joblib b/ml/silver_xgb_model/Volume_predictor.joblib index a25e1d5..8f4cbfd 100644 Binary files a/ml/silver_xgb_model/Volume_predictor.joblib and b/ml/silver_xgb_model/Volume_predictor.joblib differ 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, 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name: str) -> pd.Series: + """Fetch a closing price 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), + )