75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
symbol='SPY'
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start_date='one year ago'
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timeframe='1h'
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tz='America/New_York'
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limit=50000
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data = vbt.PolygonData.pull(
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symbol,
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start=start_date,
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timeframe=timeframe,
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tz=tz,
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limit=limit,
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missing_index="drop"
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).dropna()
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data = data.get()
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close = data['Close']
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high = data['High']
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low = data['Low']
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open = data['Open']
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vwap = data['VWAP']
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volume = data['Volume']
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@njit
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def volume_profile_numba(prices, volumes, bins=100):
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# Calculate min and max prices
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price_min, price_max = np.min(prices), np.max(prices)
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# Create price bins
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price_bins = np.linspace(price_min, price_max, bins)
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# Initialize volume sum array
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volume_sum = np.zeros(len(price_bins) - 1)
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# Bin volumes into price ranges
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for i in range(len(prices)):
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for j in range(len(price_bins) - 1):
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if price_bins[j] <= prices[i] < price_bins[j + 1]:
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volume_sum[j] += volumes[i]
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break
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# Total volume and value area volume (70%)
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total_volume = np.sum(volume_sum)
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value_area_volume = 0.7 * total_volume
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# Sort by volume to determine Value Area
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sorted_indices = np.argsort(volume_sum)[::-1]
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cum_volume = np.cumsum(volume_sum[sorted_indices])
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# Determine Value Area High (VAH) and Low (VAL)
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vah_idx = np.argmax(cum_volume >= value_area_volume)
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vah = price_bins[sorted_indices[:vah_idx + 1]].max()
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val = price_bins[sorted_indices[:vah_idx + 1]].min()
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# Point of Control (POC) - Highest volume node
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poc_idx = np.argmax(volume_sum)
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poc = price_bins[poc_idx]
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return vah, val, poc
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def get_volume_profile(data, days=7, bins=2000):
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# Extract last 'days' worth of data
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recent_data = data[-days*24:]
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prices = recent_data['Close'].values
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volumes = recent_data['Volume'].values
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vah, val, poc = volume_profile_numba(prices, volumes, bins)
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return {'VAH': vah, 'VAL': val, 'POC': poc}
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profile_levels = get_volume_profile(data, days=7)
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print(f"VAH: {profile_levels['VAH']}, VAL: {profile_levels['VAL']}, POC: {profile_levels['POC']}")
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