testing live plot
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Simon Moisy
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8f96e14b8b
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150
live_plot.py
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live_plot.py
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import json
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import threading
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import time
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import numpy as np
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import pandas as pd
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import xgboost as xgb
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from datetime import datetime
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from okx_client import OKXClient
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import dash
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from dash import dcc, html
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from dash.dependencies import Output, Input
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import plotly.graph_objs as go
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import websocket
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# --- Prediction utilities (from test_predictor.py) ---
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def add_features(df):
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df['log_return'] = np.log(df['Close'] / df['Close'].shift(1))
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def calc_rsi(close, window=14):
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delta = close.diff()
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up = delta.clip(lower=0)
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down = -1 * delta.clip(upper=0)
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ma_up = up.rolling(window=window, min_periods=window).mean()
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ma_down = down.rolling(window=window, min_periods=window).mean()
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rs = ma_up / ma_down
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return 100 - (100 / (1 + rs))
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df['rsi'] = calc_rsi(df['Close'])
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df['ema_14'] = df['Close'].ewm(span=14, adjust=False).mean()
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df['sma_50'] = df['Close'].rolling(window=50).mean()
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df['sma_200'] = df['Close'].rolling(window=200).mean()
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high_low = df['High'] - df['Low']
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high_close = np.abs(df['High'] - df['Close'].shift(1))
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low_close = np.abs(df['Low'] - df['Close'].shift(1))
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tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
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df['atr'] = tr.rolling(window=14).mean()
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df['roc_10'] = df['Close'].pct_change(periods=10) * 100
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df['dpo_20'] = df['Close'] - df['Close'].rolling(window=21).mean().shift(-10)
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df['hour'] = df['Timestamp'].dt.hour
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return df
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def load_model_and_features(model_path):
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model = xgb.Booster()
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model.load_model(model_path)
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feature_cols = [
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'rsi', 'ema_14', 'sma_50', 'sma_200', 'atr', 'roc_10', 'dpo_20', 'hour'
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]
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return model, feature_cols
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def predict_next(df, model, feature_cols):
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X = df[feature_cols].iloc[[-1]].values.astype(np.float32)
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dmatrix = xgb.DMatrix(X, feature_names=feature_cols)
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pred_log_return = model.predict(dmatrix)[0]
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last_price = df['Close'].iloc[-1]
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pred_price = last_price * np.exp(pred_log_return)
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return pred_price, pred_log_return, last_price
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# --- Main live plotting ---
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WINDOW = 50
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MODEL_PATH = 'data/xgboost_model.json'
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ohlcv_bars = [] # [timestamp, open, high, low, close, volume]
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bar_lock = threading.Lock()
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model, feature_cols = load_model_and_features(MODEL_PATH)
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# --- Background thread to collect trades and aggregate OHLCV ---
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def ws_collector():
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client = OKXClient(authenticate=False)
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client.subscribe_trades(instrument="BTC-USDT")
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current_bar = None
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while True:
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try:
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msg = client.ws.recv()
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data = json.loads(msg)
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except websocket._exceptions.WebSocketTimeoutException:
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continue # Just try again
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except Exception as e:
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print(f"WebSocket error: {e}")
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break # or try to reconnect
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if 'arg' in data and data['arg'].get('channel', '') == 'trades':
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for trade in data.get('data', []):
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# trade: {'instId', 'tradeId', 'px', 'sz', 'side', 'ts'}
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ts = int(trade['ts'])
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price = float(trade['px'])
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size = float(trade['sz'])
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dt = datetime.utcfromtimestamp(ts / 1000)
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bar_seconds = 30 # or 15, 30, etc.
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bar_time = dt.replace(second=(dt.second // bar_seconds) * bar_seconds, microsecond=0)
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with bar_lock:
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if not ohlcv_bars or ohlcv_bars[-1][0] != bar_time:
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# New bar
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ohlcv_bars.append([bar_time, price, price, price, price, size])
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if len(ohlcv_bars) > WINDOW:
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ohlcv_bars.pop(0)
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else:
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# Update current bar
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bar = ohlcv_bars[-1]
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bar[2] = max(bar[2], price) # high
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bar[3] = min(bar[3], price) # low
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bar[4] = price # close
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bar[5] += size # volume
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# Start the background thread
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threading.Thread(target=ws_collector, daemon=True).start()
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# --- Dash App ---
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app = dash.Dash(__name__)
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app.layout = html.Div([
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html.H2('BTC/USDT Price & Prediction (OKX, XGBoost, Trades Aggregated)', style={"textAlign": "center", "margin": 0, "padding": 0}),
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dcc.Graph(id='live-graph', animate=False, style={"height": "90vh", "width": "100vw", "margin": 0, "padding": 0}),
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dcc.Interval(
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id='interval-component',
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interval=5*1000, # 5 seconds
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n_intervals=0
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),
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html.Div(id='prediction-output', style={"textAlign": "center", "fontSize": 20, "marginTop": 10})
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], style={"height": "100vh", "width": "100vw", "margin": 0, "padding": 0, "overflow": "hidden", "backgroundColor": "#f7f7f7"})
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@app.callback(
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[Output('live-graph', 'figure'), Output('prediction-output', 'children')],
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[Input('interval-component', 'n_intervals')]
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)
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def update_graph_live(n):
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with bar_lock:
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bars = list(ohlcv_bars)
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if len(bars) < 2:
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return go.Figure(), "Waiting for data..."
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df = pd.DataFrame(bars, columns=["Timestamp", "Open", "High", "Low", "Close", "Volume"])
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df["Timestamp"] = pd.to_datetime(df["Timestamp"])
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df = add_features(df)
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if df[feature_cols].isnull().any().any():
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pred_text = "Not enough data for prediction."
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else:
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pred_price, pred_log_return, last_price = predict_next(df, model, feature_cols)
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pred_text = f"Last: {last_price:.2f} | Predicted next: {pred_price:.2f} | LogRet: {pred_log_return:.6f}"
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fig = go.Figure()
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fig.add_trace(go.Candlestick(
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x=df["Timestamp"],
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open=df["Open"],
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high=df["High"],
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low=df["Low"],
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close=df["Close"],
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name='Candlestick',
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increasing_line_color='green',
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decreasing_line_color='red',
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showlegend=False
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))
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fig.update_layout(title='BTC-USDT 1m OHLCV (Aggregated from Trades)', xaxis_title='Time', yaxis_title='Price (USDT)', xaxis_rangeslider_visible=False)
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return fig, pred_text
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if __name__ == '__main__':
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app.run(debug=True)
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122
test_predictor.py
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122
test_predictor.py
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import time
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import json
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import numpy as np
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import pandas as pd
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import requests
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import xgboost as xgb
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from datetime import datetime, timedelta
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# --- CONFIG ---
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OKX_REST_URL = 'https://www.okx.com/api/v5/market/candles'
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SYMBOL = 'BTC-USDT'
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BAR = '1m'
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HIST_MINUTES = 250 # Number of minutes of history to fetch for features
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MODEL_PATH = 'data/xgboost_model.json'
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# --- Fetch recent candles from OKX REST API ---
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def fetch_recent_candles(symbol, bar, limit=HIST_MINUTES):
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params = {
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'instId': symbol,
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'bar': bar,
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'limit': str(limit)
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}
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resp = requests.get(OKX_REST_URL, params=params)
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data = resp.json()
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if data['code'] != '0':
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raise Exception(f"OKX API error: {data['msg']}")
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# OKX returns most recent first, reverse to chronological
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candles = data['data'][::-1]
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df = pd.DataFrame(candles)
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# OKX columns: [ts, o, h, l, c, vol, volCcy, confirm, ...] (see API docs)
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# We'll use: ts, o, h, l, c, vol
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col_map = {
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0: 'Timestamp',
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1: 'Open',
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2: 'High',
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3: 'Low',
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4: 'Close',
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5: 'Volume',
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}
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df = df.rename(columns={str(k): v for k, v in col_map.items()})
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# If columns are not named, use integer index
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for k, v in col_map.items():
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if v not in df.columns:
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df[v] = df.iloc[:, k]
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df = df[['Timestamp', 'Open', 'High', 'Low', 'Close', 'Volume']]
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df['Timestamp'] = pd.to_datetime(df['Timestamp'].astype(np.int64), unit='ms')
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for col in ['Open', 'High', 'Low', 'Close', 'Volume']:
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df[col] = pd.to_numeric(df[col], errors='coerce')
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return df
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# --- Feature Engineering (minimal, real-time) ---
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def add_features(df):
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# Log return (target, not used for prediction)
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df['log_return'] = np.log(df['Close'] / df['Close'].shift(1))
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# RSI (14)
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def calc_rsi(close, window=14):
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delta = close.diff()
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up = delta.clip(lower=0)
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down = -1 * delta.clip(upper=0)
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ma_up = up.rolling(window=window, min_periods=window).mean()
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ma_down = down.rolling(window=window, min_periods=window).mean()
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rs = ma_up / ma_down
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return 100 - (100 / (1 + rs))
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df['rsi'] = calc_rsi(df['Close'])
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# EMA 14
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df['ema_14'] = df['Close'].ewm(span=14, adjust=False).mean()
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# SMA 50, 200
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df['sma_50'] = df['Close'].rolling(window=50).mean()
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df['sma_200'] = df['Close'].rolling(window=200).mean()
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# ATR 14
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high_low = df['High'] - df['Low']
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high_close = np.abs(df['High'] - df['Close'].shift(1))
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low_close = np.abs(df['Low'] - df['Close'].shift(1))
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tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
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df['atr'] = tr.rolling(window=14).mean()
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# ROC 10
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df['roc_10'] = df['Close'].pct_change(periods=10) * 100
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# DPO 20
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df['dpo_20'] = df['Close'] - df['Close'].rolling(window=21).mean().shift(-10)
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# Hour
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df['hour'] = df['Timestamp'].dt.hour
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# Add more features as needed (match with main.py)
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return df
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# --- Load model and feature columns ---
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def load_model_and_features(model_path):
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model = xgb.Booster()
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model.load_model(model_path)
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# Try to infer feature names from main.py (hardcoded for now)
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feature_cols = [
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'rsi', 'ema_14', 'sma_50', 'sma_200', 'atr', 'roc_10', 'dpo_20', 'hour'
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]
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return model, feature_cols
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# --- Predict next log return and price ---
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def predict_next(df, model, feature_cols):
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# Use the last row for prediction
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X = df[feature_cols].iloc[[-1]].values.astype(np.float32)
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dmatrix = xgb.DMatrix(X, feature_names=feature_cols)
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pred_log_return = model.predict(dmatrix)[0]
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last_price = df['Close'].iloc[-1]
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pred_price = last_price * np.exp(pred_log_return)
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return pred_price, pred_log_return, last_price
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if __name__ == '__main__':
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print('Fetching recent candles from OKX...')
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df = fetch_recent_candles(SYMBOL, BAR)
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df = add_features(df)
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model, feature_cols = load_model_and_features(MODEL_PATH)
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print('Waiting for new candle...')
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last_timestamp = df['Timestamp'].iloc[-1]
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while True:
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time.sleep(5)
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new_df = fetch_recent_candles(SYMBOL, BAR, limit=HIST_MINUTES)
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if new_df['Timestamp'].iloc[-1] > last_timestamp:
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df = new_df
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df = add_features(df)
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pred_price, pred_log_return, last_price = predict_next(df, model, feature_cols)
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print(f"[{df['Timestamp'].iloc[-1]}] Last price: {last_price:.2f} | Predicted next price: {pred_price:.2f} | Predicted log return: {pred_log_return:.6f}")
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last_timestamp = df['Timestamp'].iloc[-1]
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else:
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print('No new candle yet...')
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