Skip to content

Silver model update #116

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
Spectre113 merged 2 commits into from
Jul 20, 2025
Merged

Silver model update #116

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0aa6ece
Update forecasting_models.py
FaritSharafutdinov Jul 20, 2025
91e92b4
Update forecasting_models.py
FaritSharafutdinov Jul 20, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 9 additions & 5 deletions ml/forecasting_models.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1160,16 +1160,18 @@ def predict(self, date_range: pd.DatetimeIndex, initial_price: float = None) ->
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)
max_volatility = initial_price * 0.02 # 3% от начальной цены за 48 часов
max_step_change = initial_price * 0.0003 # 0.1% за шаг
min_price = initial_price * 0.93 # Минимальная цена

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_)[0]

if idx > 0:
alpha = 0.05 # Коэффициент сглаживания (0 < alpha < 1, меньшие значения - более плавное сглаживание)
y_ = alpha * y_ + (1 - alpha) * y_pred[-1]
# Умеренные колебания
atr = features['ATR14'] if 'ATR14' in features else 0.05
noise = np.random.normal(0, atr * 0.02) # Очень слабый шум
Expand Down Expand Up @@ -1220,7 +1222,9 @@ def predict(self, date_range: pd.DatetimeIndex, initial_price: float = None) ->
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)
smoothed_y_pred = y_pred.rolling(window=5, center=True, min_periods=1).mean() # Скользящее среднее с окном 5

price_prediction = initial_price + np.cumsum(smoothed_y_pred)
return price_prediction

def dump(self, path: str) -> None:
Expand Down
Loading