{{ message }}
Repository navigation
Expand file tree
/
Copy pathapi.py
More file actions
348 lines (306 loc) · 14.3 KB
/
Copy pathapi.py
File metadata and controls
348 lines (306 loc) · 14.3 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
import yfinance as yf
import pandas as pd
import numpy as np
import datetime
import requests
import os
import torch
# Import model components
import sys
sys.path.append("./")
from model import Kronos, KronosTokenizer, KronosPredictor
app = FastAPI()
# Enable CORS for the frontend
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Global variables for the model
predictor = None
@app.on_event("startup")
async def load_model():
global predictor
print("Loading Kronos model on startup...")
try:
device = 'mps' if torch.backends.mps.is_available() else ('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Selecting computing device: {device.upper()}")
tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
model = Kronos.from_pretrained("NeoQuasar/Kronos-small")
predictor = KronosPredictor(model, tokenizer, max_context=512, device=device)
print("Model loaded successfully.")
except Exception as e:
print(f"Failed to load model: {e}")
@app.get("/api/market/{ticker}")
async def get_market_data(ticker: str):
try:
stock = yf.Ticker(ticker)
info = stock.info
# Get Analyst Recommendation
rec_key = info.get('recommendationKey')
if rec_key is None:
rec_key = 'none'
rec = rec_key.replace('_', ' ').title()
if rec.lower() == 'none':
rec = "N/A"
# Calculate basic volatility (30-day standard deviation)
hist = stock.history(period="1mo")
volatility = "N/A"
if not hist.empty and len(hist) > 1:
returns = hist['Close'].pct_change().dropna()
volatility = f"{returns.std() * np.sqrt(252) * 100:.1f}%"
return {
"symbol": ticker,
"current_price": info.get("currentPrice") or info.get("regularMarketPrice") or (hist['Close'].iloc[-1] if not hist.empty else 0),
"pe_ratio": info.get("trailingPE", "N/A"),
"beta": info.get("beta", "N/A"),
"volatility": volatility,
"market_cap": info.get("marketCap", "N/A"),
"volume": info.get("volume", "N/A"),
"analyst_rating": rec
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/news")
async def get_news_analysis():
# In a real scenario, this would trigger the Antigravity agent.
# For now, we'll fetch real news from yfinance (SPY as a proxy for macro) and ForexFactory,
# and provide a synthesized "Agent Output".
try:
# 1. Fetch ForexFactory macro calendar
headers = {'User-Agent': 'Mozilla/5.0'}
r = requests.get("https://nfs.faireconomy.media/ff_calendar_thisweek.json", headers=headers)
calendar = []
if r.status_code == 200:
events = r.json()
# Filter high impact events today/tomorrow
today = datetime.datetime.now().date()
for event in events:
if event['impact'] == 'High':
try:
# Attempt to parse time from ISO format if it exists
evt_time_str = event.get('date', '')
if evt_time_str:
dt = datetime.datetime.fromisoformat(evt_time_str.replace('Z', '+00:00'))
time_str = dt.strftime('%I:%M %p')
else:
time_str = 'Pending'
except:
time_str = 'Pending'
calendar.append({
"time": time_str,
"country": event.get('country', ''),
"title": event.get('title', ''),
"actual": event.get('actual', ''),
"forecast": event.get('forecast', ''),
"previous": event.get('previous', '')
})
if len(calendar) >= 4:
break
# 2. Fetch general market news
spy = yf.Ticker("SPY")
news_items = spy.news[:5]
headlines = [item['title'] for item in news_items if 'title' in item]
return {
"headlines": headlines,
"macro_events": calendar,
"agent_analysis": "The market is currently showing mixed signals. High-impact macro events are pending, creating a cautious environment. Volatility remains elevated in tech and precious metals.",
"uncertainty_level": "Medium-High",
"sentiment_score": 65
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/forecast/{ticker}")
async def get_forecast(ticker: str):
global predictor
if predictor is None:
raise HTTPException(status_code=503, detail="Model is not loaded yet")
try:
# Download data
stock = yf.Ticker(ticker)
df = stock.history(period="60d", interval="5m")
if df.empty:
raise HTTPException(status_code=404, detail="No data found for ticker")
df.reset_index(inplace=True)
# Rename Date to timestamps if needed (sometimes it's Datetime)
if 'Datetime' in df.columns:
df.rename(columns={'Datetime': 'timestamps'}, inplace=True)
elif 'Date' in df.columns:
df.rename(columns={'Date': 'timestamps'}, inplace=True)
df.columns = [c.lower() for c in df.columns]
df['timestamps'] = pd.to_datetime(df['timestamps'])
lookback = 400
pred_len = 120
# Features
if len(df) < lookback:
x_df = df.copy()
x_timestamp = df['timestamps'].copy()
else:
x_df = df.iloc[-lookback:].reset_index(drop=True)[['open', 'high', 'low', 'close', 'volume']]
x_timestamp = df.iloc[-lookback:]['timestamps'].reset_index(drop=True)
x_df['amount'] = x_df['volume'] * x_df[['open', 'high', 'low', 'close']].mean(axis=1)
last_timestamp = x_timestamp.iloc[-1]
y_timestamp = pd.Series(pd.date_range(start=last_timestamp + pd.Timedelta(minutes=5), periods=pred_len, freq='5min'))
# Inference
pred_df = predictor.predict(
df=x_df,
x_timestamp=x_timestamp,
y_timestamp=y_timestamp,
pred_len=pred_len,
T=1.0,
top_p=0.9,
sample_count=1,
verbose=False
)
# Format output for lightweight-charts
# Lightweight charts expects {time: unix_timestamp, open, high, low, close}
def format_for_chart(df_part, time_series):
result = []
for i in range(len(df_part)):
t = time_series.iloc[i]
if hasattr(t, 'tz_localize') and t.tzinfo is not None:
t = t.tz_localize(None)
# UNIX timestamp in seconds
unix_time = int(t.timestamp())
result.append({
"time": unix_time,
"open": float(df_part['open'].iloc[i]),
"high": float(df_part['high'].iloc[i]),
"low": float(df_part['low'].iloc[i]),
"close": float(df_part['close'].iloc[i])
})
return result
historical_data = format_for_chart(x_df.iloc[-100:], x_timestamp.iloc[-100:])
prediction_data = format_for_chart(pred_df, y_timestamp)
# Calculate best time to buy and sell (LONG)
buy_time_long, sell_time_long = None, None
buy_price_long, sell_price_long = 0, 0
max_profit_long = -float('inf')
# Calculate best time to sell and buy back (SHORT)
sell_time_short, buy_time_short = None, None
sell_price_short, buy_price_short = 0, 0
max_profit_short = -float('inf')
if len(prediction_data) > 1:
# LONG logic
min_price_idx = 0
for i in range(1, len(prediction_data)):
if prediction_data[i]['close'] < prediction_data[min_price_idx]['close']:
min_price_idx = i
profit = prediction_data[i]['close'] - prediction_data[min_price_idx]['close']
if profit > max_profit_long and i > min_price_idx:
max_profit_long = profit
buy_time_long = prediction_data[min_price_idx]['time']
sell_time_long = prediction_data[i]['time']
buy_price_long = prediction_data[min_price_idx]['close']
sell_price_long = prediction_data[i]['close']
# SHORT logic
max_price_idx = 0
for i in range(1, len(prediction_data)):
if prediction_data[i]['close'] > prediction_data[max_price_idx]['close']:
max_price_idx = i
# Profit for short is sell_price - buy_price (buying back lower)
profit = prediction_data[max_price_idx]['close'] - prediction_data[i]['close']
if profit > max_profit_short and i > max_price_idx:
max_profit_short = profit
sell_time_short = prediction_data[max_price_idx]['time']
buy_time_short = prediction_data[i]['time']
sell_price_short = prediction_data[max_price_idx]['close']
buy_price_short = prediction_data[i]['close']
signal = None
trade_type = "LONG"
if max_profit_long > 0 or max_profit_short > 0:
if max_profit_long >= max_profit_short:
trade_type = "LONG"
entry_time = buy_time_long
exit_time = sell_time_long
entry_price = buy_price_long
exit_price = sell_price_long
expected_profit_pct = (max_profit_long / entry_price) * 100
take_profit = exit_price
# SL for LONG: minimum predicted low between entry and exit
min_between = entry_price
start_counting = False
for p in prediction_data:
if p['time'] == entry_time: start_counting = True
if start_counting:
if p['low'] < min_between: min_between = p['low']
if p['time'] == exit_time: break
stop_loss = min_between * 0.995
if stop_loss > entry_price * 0.99:
stop_loss = entry_price * 0.99
else:
trade_type = "SHORT"
entry_time = sell_time_short
exit_time = buy_time_short
entry_price = sell_price_short
exit_price = buy_price_short
expected_profit_pct = (max_profit_short / entry_price) * 100
take_profit = exit_price
# SL for SHORT: maximum predicted high between entry and exit
max_between = entry_price
start_counting = False
for p in prediction_data:
if p['time'] == entry_time: start_counting = True
if start_counting:
if p['high'] > max_between: max_between = p['high']
if p['time'] == exit_time: break
stop_loss = max_between * 1.005
if stop_loss < entry_price * 1.01:
stop_loss = entry_price * 1.01
# Simulated £1000 Portfolio
portfolio_size = 1000
units = portfolio_size / entry_price
potential_profit = units * abs(take_profit - entry_price)
potential_loss = units * abs(entry_price - stop_loss)
# Generate Dynamic Drivers & Risks based on technicals
duration_hours = (exit_time - entry_time) / 3600
# Determine momentum characteristic
if trade_type == 'LONG':
if entry_time == y_timestamp.iloc[0]:
momentum_reason = "Model identifies immediate accumulation momentum, anticipating a breakout from current consolidation levels."
else:
momentum_reason = "Model predicts a mean-reverting bounce; price action is expected to sweep liquidity lower before reversing upward."
else:
if entry_time == y_timestamp.iloc[0]:
momentum_reason = "Model identifies immediate distribution pressure, anticipating a breakdown from current resistance."
else:
momentum_reason = "Model predicts a mean-reverting rejection; price action is expected to spike into liquidity before reversing downward."
drivers = [
f"Kronos projects a highly-probable {expected_profit_pct:.2f}% {'upside' if trade_type == 'LONG' else 'downside'} swing.",
momentum_reason,
f"Target (TP) realization expected within {duration_hours:.1f} hours from entry, capitalizing on predicted volatility waves."
]
risks = [
f"Pending high-impact macro events could override technical forecasts.",
f"Maximum drawdown risk limited to -£{potential_loss:.2f} at strict SL of ${stop_loss:.2f}."
]
signal = {
"type": trade_type,
"entry_time": entry_time,
"exit_time": exit_time,
"entry_price": entry_price,
"take_profit": take_profit,
"stop_loss": stop_loss,
"expected_profit_pct": expected_profit_pct,
"portfolio_profit_gbp": potential_profit,
"portfolio_risk_gbp": potential_loss,
"drivers": drivers,
"risks": risks
}
return {
"symbol": ticker,
"historical": historical_data,
"prediction": prediction_data,
"signal": signal
}
except Exception as e:
import traceback
traceback.print_exc()
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
import uvicorn
uvicorn.run("api:app", host="0.0.0.0", port=8000, reload=True)
You can’t perform that action at this time.