Shorting Cryptos for Fun & Profit with the SNX10 Index Fund
An Exchange-Traded Tokenized Asset by Vectorspace AI X
Institutions like BlackRock, Fidelity, Goldman Sachs and others are busy trying to tokenize assets these days by creating exchange-traded index funds. However, making money when a crypto or stock goes down remains one of the holy grails of finance in the truly global marketplace of crypto and stocks. The SNX10 is an exchange-traded tokenized asset and trading vehicle prices using a standard ‘index options’ pricing model which can be used to do this, similar to the list of publicly traded index funds and ETFs at the end of this article.
The SNX10 is an AI-driven index fund or ‘Bear Index’ which shorts cryptos similar to an ‘inverse ETF’ for stocks. It’s also a trading vehicle and financial product developed by Vectorspace AI X.
The SNX10 index fund is powered by a specific AI model which generates an index of cryptos or stocks based on their relationships to a theme and allocates capital to maximize returns.
The AI model and SNX10 index fund is a tokenized asset based on proprietary IP (Intellectual Property) with the trading symbol: ‘SNX10’ initially trading on the ProBit Exchange, soon to be available on most major exchanges.
Trading the SNX10 enables you to invest in the hedge fund’s AI-driven strategies including contests enabling investment into a hedge fund.
The SNX10 can be used by traders to speculate on or hedge volatility.
Assets Under Management (AUM) for the SNX10 index fund will be based on AI modeled weighted capital allocations across components (cryptos/stocks/options/assets) in the index.
Market Makers (MMs) and institutions used by the hedge fund ensure price support and liquidity on the SNX10 based on capital inflow and keeping the price of the SNX10 closely tracking the Net Asset Value (NAV) of the underlying cryptos or stocks.
Capital Flow Diagram:
The Contest
Our hedge fund will be holding ongoing contests surrounding the SNX10 and its AI models. The data from the contests enables each AI model to be optimized for trading and hedge fund operations. The fair market price of the SNX10 is based on a standard ‘index options’ pricing model using the cryptos or stocks held in the index combined with a premium of 5% based on optimizations such as agentic weighted capital allocation algorithms, community-driven human-in-the-loop validation and other indicator weights including overbought vs oversold conditions.
SNX10 contests are held for every index option expiration date which expire every 1, 3, 5, 7, 10, 15, 30 and 90 day periods and enable any SNX10 holder to:
Sign up for the contest to predict SNX10 index returns for any number of periods on our Telegram channel here.
Enter a prediction on SNX10 returns and/or total volume for a given periods with 1st, 2nd and 3rd prizes for each prediction in 100, 50 and 10 SNX10 tokens for those closest to the actual returns of the index for that period.
Trade the SNX10 and profit if it goes down as described here.
Trade the SNX10 long and profit if it goes up.
For a limited time all VAIX and SBIO holders with a minimum of 1,000 tokens receive a 50% bonus on any SNX10 acquired (returns settled or paid out in VAIX optional).
Additional details including registration for the contest can be found here.
How the Price of the SNX10 is Calculated: The Index Options Pricing Model
To calculate a crypto or stock index option price, parameters are used such as:
Current prices
Volatility metrics
Strike prices
Time to expiration
Risk-free interest rate
Any other relevant parameters for the option pricing model you want to use, in our case weights from AI models and capital allocation algorithms.
The index options price is calculated using an appropriate model (such as Black-Scholes or a model more specific to crypto options).
In the following example, price and market cap of the following cryptos (memecoins) were used for the date of March 10, 2025: "BABYDOGE", "BONK", "MEOW", "KET", "AIC", "AI16Z", "SNEK", "NOT", "POPCAT", "WIF"
Equal-Weighted Index:
Index Option Value: 0.115479
Annualized Volatility: 30.72%
Option Prices (30-day expiration):
90% Strike (0.103931): Call = 0.015481, Put = 0.003932
100% Strike (0.115479): Call = 0.009677, Put = 0.008129
110% Strike (0.127027): Call = 0.005711, Put = 0.014163
Market Cap Weighted Index:
Index Option Value: 0.187542
Annualized Volatility: 28.25%
Option Prices (30-day expiration):
90% Strike (0.168788): Call = 0.023478, Put = 0.004724
100% Strike (0.187542): Call = 0.014843, Put = 0.012089
110% Strike (0.206296): Call = 0.008796, Put = 0.022042
These calculations include:
Black-Scholes pricing for both call and put options
Volatility estimates derived from 7-day price changes
Options priced for three strike prices (90%, 100%, and 110% of index value)
Both equal-weighted and market cap weighted index approaches
In addition to proprietary modeling providing:
More sophisticated volatility models
Liquidity premiums
Funding rates
Skew adjustments
Python code to calculate the index options price based on the above data:
import numpy as np
import pandas as pd
from scipy.stats import norm
import math
# Function for Black-Scholes option pricing model
def black_scholes_call(S, K, T, r, sigma):
"""
Calculate Black-Scholes price for a call option
Parameters:
S: Current price of the underlying asset
K: Strike price
T: Time to expiration (in years)
r: Risk-free interest rate
sigma: Volatility of the underlying asset
Returns:
call_price: Price of the call option
"""
d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))
d2 = d1 - sigma * np.sqrt(T)
call_price = S * norm.cdf(d1) - K * np.exp(-r * T) * norm.cdf(d2)
return call_price
def black_scholes_put(S, K, T, r, sigma):
"""
Calculate Black-Scholes price for a put option
Parameters:
S: Current price of the underlying asset
K: Strike price
T: Time to expiration (in years)
r: Risk-free interest rate
sigma: Volatility of the underlying asset
Returns:
put_price: Price of the put option
"""
d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))
d2 = d1 - sigma * np.sqrt(T)
put_price = K * np.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)
return put_price
# Parse the cryptocurrency data
data = {
"Coin": ["Baby Doge Coin", "Bonk", "MEOW", "Ket", "AI Companions", "ai16z", "Snek", "Notcoin", "Popcat", "dogwifhat"],
"Symbol": ["BABYDOGE", "BONK", "MEOW", "KET", "AIC", "AI16Z", "SNEK", "NOT", "POPCAT", "WIF"],
"Price": [0.08111, 0.05994, 0.00186, 0.1351, 0.2477, 0.1891, 0.00286, 0.00201, 0.1566, 0.4464],
"Change_1h": [1.1, 0.2, 2.2, 1.7, 0.3, 1.4, 0.0, 0.3, 0.4, 0.2],
"Change_24h": [0.4, 2.9, 14.5, 7.4, 2.0, 3.7, 7.2, 1.7, 8.0, 0.1],
"Change_7d": [14.7, 20.4, 53.3, 56.1, 18.5, 43.5, 29.6, 25.4, 33.5, 31.6],
"Market_Cap": [184293561, 773939416, 166934356, 135657227, 247491693, 209198379, 211028418, 207334949, 153673564, 446433430]
}
df = pd.DataFrame(data)
# Calculate implied volatility based on 7-day price changes
# Note: This is a simplified approach. In practice, would use a more sophisticated method
df['Volatility'] = df['Change_7d'] / 100
# Annualize volatility (approximate method)
df['Annualized_Volatility'] = df['Volatility'] * math.sqrt(52) # 52 weeks in a year
# Constants for option pricing
risk_free_rate = 0.05 # 5% assumed risk-free rate
time_to_expiration = 30/365 # 30 days option
# Create index weights (equal weighting for simplicity)
# In practice, would typically use market cap weighting or another approach
df['Weight'] = 1 / len(df)
# Calculate index value
index_value = sum(df['Price'] * df['Weight'])
# Calculate index volatility (weighted average of individual volatilities)
index_volatility = sum(df['Annualized_Volatility'] * df['Weight'])
# Calculate option prices for different strike prices
strikes = [index_value * 0.9, index_value, index_value * 1.1] # 90%, 100%, and 110% of index value
option_results = []
for strike in strikes:
call_price = black_scholes_call(index_value, strike, time_to_expiration, risk_free_rate, index_volatility)
put_price = black_scholes_put(index_value, strike, time_to_expiration, risk_free_rate, index_volatility)
option_results.append({
'Strike': strike,
'Call_Price': call_price,
'Put_Price': put_price
})
# Create final results DataFrame
option_prices = pd.DataFrame(option_results)
# Display results
print("Cryptocurrency Index Value:", round(index_value, 6))
print("Index Annualized Volatility:", round(index_volatility * 100, 2), "%")
print("\nOption Prices (30-day expiration):")
print(option_prices.round(6))
# Market cap weighted index calculation (alternative approach)
df['MC_Weight'] = df['Market_Cap'] / sum(df['Market_Cap'])
mc_index_value = sum(df['Price'] * df['MC_Weight'])
mc_index_volatility = sum(df['Annualized_Volatility'] * df['MC_Weight'])
print("\nMarket Cap Weighted Index Value:", round(mc_index_value, 6))
print("Market Cap Weighted Index Volatility:", round(mc_index_volatility * 100, 2), "%")
# Calculate option prices for market cap weighted index
mc_option_results = []
mc_strikes = [mc_index_value * 0.9, mc_index_value, mc_index_value * 1.1]
for strike in mc_strikes:
call_price = black_scholes_call(mc_index_value, strike, time_to_expiration, risk_free_rate, mc_index_volatility)
put_price = black_scholes_put(mc_index_value, strike, time_to_expiration, risk_free_rate, mc_index_volatility)
mc_option_results.append({
'Strike': strike,
'Call_Price': call_price,
'Put_Price': put_price
})
mc_option_prices = pd.DataFrame(mc_option_results)
print("\nMarket Cap Weighted Option Prices (30-day expiration):")
print(mc_option_prices.round(6))Historical returns and reports including Assets Under Management (AUM) will be published on a regular basis.
SNX10 Statistics:
Name: AI-Driven Capital Weighted Short Index Option Fund for 10 Cryptos (SNX10) - by Vectorspace AI X
Symbol: SNX10
Price: (standard index option pricing model combined with AI-driven modeling for weighted capital allocation across 10 cryptos and/or stocks)
Max supply: 1B
Token type: ERC-20
Contract address: 0x7d5acab282bcd4bd7d3a785b73cfaa359a6526d5
Popular Publicly Traded Index Funds, Themes, Baskets & ETFs:
Note: BlackRock alone has over 450 index funds and ETFs as shown here. Some of them include:
As of March 2025, Invesco offers over 500 exchange-traded funds (ETFs), encompassing a diverse range of investment strategies and asset classes. These index funds include both index-based and actively managed funds.
Most Popular:
The VIX index currently trades at $24.00 and is the symbol and the popular name for the Chicago Board Options Exchange's CBOE Volatility Index, a popular measure of the stock market's expectation of volatility based on S&P 500 index options.
The QQQ index currently trades at $476.00 (also known by its full index fund name Invesco QQQ Trust, Series 1), is an exchange-traded fund created by Invesco PowerShares. The QQQ index tracks the performance of the Nasdaq-100.
The SPY index currently trades at $558.00 is the symbol for the SPDR S&P 500 ETF Trust: One of the oldest and largest ETFs, it tracks the S&P 500 Index, representing large-cap U.S. equities.
Broad Market ETFs:
Vanguard Total Stock Market ETF (VTI): Tracks the performance of the CRSP U.S. Total Market Index, representing nearly 100% of the U.S. investable equity market.
iShares Core S&P Total U.S. Stock Market ETF (ITOT): Replicates the S&P Total Market Index, covering large, mid, small, and micro-cap U.S. equities.
Schwab U.S. Broad Market ETF (SCHB): Follows the Dow Jones U.S. Broad Stock Market Index, encompassing the entirety of the U.S. stock market.
Large-Cap ETFs:
SPDR S&P 500 ETF Trust (SPY): One of the oldest and largest ETFs, it tracks the S&P 500 Index, representing large-cap U.S. equities.
Vanguard S&P 500 ETF (VOO): Offers exposure to the S&P 500 Index with a low expense ratio.
iShares Core S&P 500 ETF (IVV): Another fund that tracks the S&P 500 Index, known for its liquidity and low costs.
Mid-Cap ETFs:
iShares S&P Mid-Cap 400 ETF (IJH): Replicates the S&P MidCap 400 Index, covering U.S. mid-cap stocks.
Vanguard Mid-Cap ETF (VO): Tracks the CRSP U.S. Mid Cap Index, providing exposure to medium-sized companies.
Small-Cap ETFs:
iShares Russell 2000 ETF (IWM): Follows the Russell 2000 Index, representing small-cap U.S. equities.
Vanguard Small-Cap ETF (VB): Replicates the CRSP U.S. Small Cap Index, offering broad small-cap exposure.
International ETFs:
Vanguard Total International Stock ETF (VXUS): Tracks the performance of the FTSE Global All Cap ex US Index, covering international equities outside the U.S.
iShares MSCI ACWI ex U.S. ETF (ACWX): Replicates the MSCI ACWI ex USA Index, providing exposure to global markets excluding the U.S.
Sector-Specific ETFs:
Technology Select Sector SPDR Fund (XLK): Targets the technology sector within the S&P 500.
Financial Select Sector SPDR Fund (XLF): Focuses on the financial sector of the S&P 500.
Bond ETFs:
iShares Core U.S. Aggregate Bond ETF (AGG): Tracks the Bloomberg Barclays U.S. Aggregate Bond Index, representing the total U.S. investment-grade bond market.
Vanguard Total Bond Market ETF (BND): Replicates the Bloomberg Barclays U.S. Aggregate Float Adjusted Bond Index, covering a wide range of public, investment-grade, taxable, fixed-income securities in the U.S.
Other Sector ETFs
Industrials Select Sector SPDR Index Fund
Health Care Select Sector SPDR Index Fund
Communication Services Select Sector SPDR Index Fund
Consumer Staples Select Sector SPDR Index Fund
Energy Select Sector SPDR Index Fund
Materials Select Sector SPDR Index Fund
At Vectorspace AI X, Our job is find more ways for you to make money with our AI modeled financial market products and trading vehicles which are tokenized assets. As this happens, the SNX10 short index will be made available to retail and institutions that offer financial products such as:
ETFs, index funds for stocks, cryptos and options
Data providers
Market research firms
Hedge funds
Trader bases of the individual cryptos or stocks in the SNX10.
For more information contact: Telegram, vectorspacebios.science/finance, or support@vectorspace.ai
Stay tuned!