About CNBC Trading
The CNBC Trading dataset by Quiver Quantitative tracks the recommendations made by media personalities on CNBC and their historical performance. The data covers over 1,500 US Equities, starts in December 2020, and is delivered on a daily frequency. This dataset covers recommendations made on Mad Money, Halftime Report, and Fast Money.
This dataset depends on the US Equity Security Master dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
About Quiver Quantitative
Quiver Quantitative was founded by two college students in February 2020 with the goal of bridging the information gap between Wall Street and non-professional investors. Quiver allows retail investors to tap into the power of big data and have access to actionable, easy to interpret data that hasn’t already been dissected by Wall Street.
About QuantConnect
QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
Algorithm Example
from AlgorithmImports import *
from QuantConnect.DataSource import *
class QuiverCNBCsAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2021, 10, 1) #Set Start Date
self.set_end_date(2021, 10, 31) #Set End Date
self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
# Subscribe to CNBC data for AAPL to generate trade signal
self.dataset_symbol = self.add_data(QuiverCNBCs, self.aapl).symbol
# history request
history = self.history(self.dataset_symbol, 10, Resolution.DAILY)
self.debug(f"We got {len(history)} items from historical data request of {self.dataset_symbol}.")
def on_data(self, slice: Slice) -> None:
for cnbcs in slice.Get(QuiverCNBCs).values():
# Using mean prediction from CNBC analysts to be the trade signal
# If the average CNBC insight is upward movement, invest AAPL
if np.mean([cnbc.direction for cnbc in cnbcs]) > 0:
self.set_holdings(self.aapl, 1)
else:
self.set_holdings(self.aapl, 0)
Example Applications
The Quiver Quantitative CNBC Trading dataset enables you to create strategies using the latest recommendations made by media personalities on CNBC. Examples include the following strategies:
- Taking short positions in securities that were mentioned by Jim Cramer (CNBC commentator) in the last week
- Trading securities that were most/least discussed across CNBC programs over the last year
Pricing
Cloud Access
Harness CNBC Trading data in the QuantConnect Cloud for your backtesting and live trading purposes.
On Premise Download
Download CNBC Trading historical records for your LEAN backtesting on premise with the LEAN CLI.
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