Calculate VWAP and RSI

Overview

In this example we will use the Historical client to calculate two technical indicators: the volume weighted average price (VWAP) and relative strength indicator (RSI). To do this we will request the open, high, low, and close prices in one minute intervals along with aggregated volume data to calculate these indicators at one minute intervals.

OHLCV-1m schema

We'll demonstrate this example using the OHLCV-1m schema. The OHLCV family of schemas contain the opening, high, low and closing prices as well as the aggregated volume of trades within a time interval. We'll use an interval of one minute, a typical charting frequency, which is specified by the suffix -1m.

Example

Python

      
    
import databento as db

# First, create a historical client
client = db.Historical("$YOUR_API_KEY")

# Next, we will request the OHLCV-1m data
data = client.timeseries.get_range(
    dataset="GLBX.MDP3",
    start="2022-09-20",
    symbols="CLV2",
    stype_in="raw_symbol",
    schema="ohlcv-1m",
)

# Then, convert the DBNStore to a DataFrame
ohlcv_data = data.to_df()

# Now, we will calculate the volume weighted average price (VWAP)
ohlcv_data["pvt"] = ohlcv_data[["open", "high", "close"]].mean(axis=1) * ohlcv_data["volume"]
ohlcv_data["vwap"] = ohlcv_data["pvt"].cumsum() / ohlcv_data["volume"].cumsum()

# Finally, we will calculate the relative strength indicator (RSI)
ohlcv_data["up"] = (ohlcv_data["close"] - ohlcv_data["open"]).clip(lower=0)
ohlcv_data["down"] = (ohlcv_data["open"] - ohlcv_data["close"]).clip(lower=0)

# We will use a simple moving average for this example
ohlcv_data["rs"] = ohlcv_data["up"].rolling(5).mean() / ohlcv_data["down"].rolling(5).mean()
ohlcv_data["rsi"] = 100 - (100 / (1 + ohlcv_data["rs"]))

# We now have a DataFrame containing our technical indicators
print(ohlcv_data[["symbol", "open", "close", "vwap", "rsi"]])

Result

Python

      
    
                          symbol   open  close       vwap        rsi
ts_event
2022-09-20 00:00:00+00:00   CLV2  85.85  85.85  85.850000        NaN
2022-09-20 00:08:00+00:00   CLV2  86.05  86.01  85.938889        NaN
2022-09-20 00:10:00+00:00   CLV2  86.03  86.03  85.950278        NaN
2022-09-20 00:11:00+00:00   CLV2  86.03  85.98  85.964516        NaN
2022-09-20 00:12:00+00:00   CLV2  86.01  86.01  85.965938   0.000000
...                          ...    ...    ...        ...        ...
2022-09-20 18:25:00+00:00   CLV2  84.27  84.40  84.532655  53.488372
2022-09-20 18:26:00+00:00   CLV2  84.41  84.32  84.529908  51.111111
2022-09-20 18:27:00+00:00   CLV2  84.32  84.33  84.526147  64.864865
2022-09-20 18:28:00+00:00   CLV2  84.33  84.40  84.523789  77.500000
2022-09-20 18:29:00+00:00   CLV2  84.30  84.19  84.523212  51.219512

[176 rows x 5 columns]