Storyfuel
Price Calculations
Price Calculations
The StoryFuel Treasury, as the issuer of StoryFuel, can choose the issuance price. The two foundational approaches either use a market price and use the aggregate of creator prices.
The market price (PmP) is typically determined by supply and demand. In a simple model:
Pm=f(S,D)
Where:
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Pm = Market Price
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S = Supply of the currency
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D = Demand for the currency
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f = Function representing the relationship between supply, demand, and price. This is often complex in real markets due to various factors affecting both supply and demand.
The StoryFuel price (in blue) increases over time, reflecting growth in demand, increase in treasury revenue, increase in valuation and decrease in supply.
Example of simulated price movements in year 10 based on market pricing.
Over time, we may move to network issuer pricing
In this approach, the issuer sets the currency price based on the aggregate of all market participants' (in this case, content creators) input pricing. Essentially, each participant provides a price at which they'd accept a unit of StoryFuel, and an average or weighted average is taken to determine the issuance price.
Simple Average Price: If each content creator provides a price at which they would accept a unit of the currency, a simple average can be taken:
Pa=∑i=1nPin
Where:
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Pa = Average Price
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Pi = Price given by the i-th content creator
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n = Total number of content creators
Weighted Average Price: If there's a need to give certain content creators more influence on the price (maybe based on their contribution, reputation, etc.), a weighted average can be taken:
Pw=∑i=1nwi×Pi∑i=1nwi
Where:
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Pw = Weighted Average Price
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Wi = Weight for the i-th content creator (sum of all weights should ideally equal 1)
The Monte Carlo simulation is a computational technique that allows for the assessment of risk and uncertainty in predictive modeling. In the context of forecasting the 20-year price trajectory of StoryFuel, this method was employed to generate a range of possible future price paths based on historical data and specified assumptions.
For the simulation, the primary input variables were the Demand Ratio (DR), Earnings Ratio (ER), and Creator Ratio (CR). Each of these ratios was assumed to follow a normal distribution, with their mean and standard deviation derived from the historical dataset provided. Additionally, the price for each year was influenced by the price of the preceding year, incorporating the effects of cumulative changes over time.