Michael Zargham
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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 # Power-Invariant Bonding Curve Derivations and Definitions ## Motivation Analysis of Augmented Bonding curve subsystem for "Commons Stack" ![](https://i.imgur.com/3Ov4H8N.jpg) ## Export from Python notebook by mZargham Notebook header cell: ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns %matplotlib inline ``` ### System timeline - hatch sale: raise funds to initialize curve - funded reference implementation launch - post lauch system evolution modeled elsewhere ![](https://i.imgur.com/2eTfRey.jpg) ![](https://i.imgur.com/qA344p0.jpg) ![](https://i.imgur.com/oczA0Gb.jpg) ### Hatch Sale Equations - Initial Raise $d_0$ Dai - Initial split between the Bonding Curve Reserve and the Funding pool is $\theta$ - Initial Reserve is $R_0 = (1-\theta) d_0$ - Hatch sale Price $p_0$ (dai per token) determines the initial supply: $S_0 = d_0/p_0$ - Power Function Invariant shape: $V(R, S) = \frac{S^\kappa}{R}$ - Price function (DAI per Token): $P(R) = \frac{\kappa R^{(\kappa-1)/ \kappa}}{V_0^{1 / \kappa}}$ - Supply function (tokens): $S(R) = \sqrt[\kappa]{V_0 R}$ - Reserve function (xDAI): $R(S) = \frac{S^{\kappa}}{V_0}$ - The invariant coef: $V_0 = V(R_0, S_0) = \frac{S_0^\kappa}{R_0} = \left(\frac{1}{p_0(1-\theta)}\right)^\kappa R_0^{\kappa-1}$ - The post hatch price: $p_1=P(R_0) = \frac{\kappa R_0^{(\kappa-1)/ \kappa}}{V_0^{1 / \kappa}} = \kappa R_0^{(\kappa-1)/ \kappa} \cdot(1-\theta)p_0\cdot R_0^{-(\kappa-1)/\kappa} = \kappa(1-\theta) p_0$ - The Return factor: $\frac{p_1}{p_0} = {\kappa}(1-\theta)$ ### Invariant Preserving Bond-to-Mint - Deposit $\Delta R$ xdai - Conservation equation: $V(R+ \Delta R, S+\Delta S) = \frac{(S+\Delta S)^\kappa}{R+\Delta R} =V_0$ - Derived Mint equation: $\Delta S = mint\big(\Delta R ; (R,S)\big)= \sqrt[\kappa]{V_0(R+\Delta R)}-S$ - Realized Price is: $\bar{P}(\Delta R) =\frac{\Delta R}{\Delta S} = \frac{\Delta R}{\sqrt[\kappa]{V_0(R+\Delta R)}-\sqrt[\kappa]{V_0(R)}} \rightarrow \big(\frac{\partial S(R)}{\partial R} \big)^{-1}$ as $\Delta R \rightarrow 0$ - The limiting price is the spot price: $\lim_{\Delta R \rightarrow 0} \bar{P}(\Delta R)=\big(\frac{\partial S(R)}{\partial R}\big)^{-1}= \big(\frac{V_0^{1/\kappa} \cdot R^{1/\kappa-1}}{\kappa}\big)^{-1}= \frac{\kappa R^{1-1/\kappa}}{V_0^{1/\kappa}} = \frac{\kappa R^{(\kappa-1)/\kappa}}{V_0^{1/\kappa}} =P(R)$ ### Invariant Preserving Burn-to-Withdraw - Burn $\Delta S$ tokens - Conservation equation: $V(R- \Delta R, S-\Delta S) = \frac{(S-\Delta S)^\kappa}{R-\Delta R} =V_0$ - Derived Withdraw equation: $\Delta R = withdraw\big(\Delta S ; (R,S)\big)= R-\frac{(S-\Delta S)^\kappa}{V_0}$ - Realized Price is: $\bar{P}(\Delta S) =\frac{\Delta R}{\Delta S} = \frac{\frac{S^{\kappa}}{V_0}-\frac{(S-\Delta S)^\kappa}{V_0}}{\Delta S} \rightarrow \frac{\partial R(S)}{\partial S}$ as $\Delta S \rightarrow 0$ - The limiting price is the spot price: $\lim_{\Delta S \rightarrow 0} \bar{P}(\Delta S)=\frac{\partial R(S)}{\partial S}=\frac{\kappa S^{\kappa-1}}{V_0} = \frac{\kappa \cdot (\sqrt[\kappa]{V_0 R})^{\kappa-1}}{V_0}= \frac{\kappa R^{(\kappa-1)/\kappa}}{V_0^{1/\kappa}}=P(R)$ - Given friction coef $\phi$ - sent to burning agent address: $\Delta R_{agent} = (1-\phi) \Delta R$ - sent to the funding pool address: $\Delta R_{pool} = \phi \Delta R$ - due to the friction the true realized price for the agent is $(1-\phi)\cdot \bar{P}(\Delta S)$ - due to the friction the true return factor post withdraw is: ${\kappa}(1-\theta)(1-\phi)$ - note that immediate withdraw will not be available for hatch sale participants; tokens will vest slowly as a precaution against large batch dumping. Details of vesting schedules and related dynamics modeled elsewhere. ```python #integer_units = 10**12 #account for decimal places to a token #scale_units = 10**6 #millions of tokens, million of DAI #mu = integer_units*scale_units #value function for a given state (R,S) def invariant(R,S,kappa): return (S**kappa)/R #given a value function (parameterized by kappa) #and an invariant coeficient V0 #return Supply S as a function of reserve R def supply(R, kappa, V0): return (V0*R)**(1/kappa) #given a value function (parameterized by kappa) #and an invariant coeficient V0 #return a spot price P as a function of reserve R def spot_price(R, kappa, V0): return kappa*R**((kappa-1)/kappa)/V0**(1/kappa) #for a given state (R,S) #given a value function (parameterized by kappa) #and an invariant coeficient V0 #deposit deltaR to Mint deltaS #with realized price deltaR/deltaS def mint(deltaR, R,S, kappa, V0): deltaS = (V0*(R+deltaR))**(1/kappa)-S realized_price = deltaR/deltaS return deltaS, realized_price #for a given state (R,S) #given a value function (parameterized by kappa) #and an invariant coeficient V0 #burn deltaS to Withdraw deltaR #with realized price deltaR/deltaS def withdraw(deltaS, R,S, kappa, V0): deltaR = R-((S-deltaS)**kappa)/V0 realized_price = deltaR/deltaS return deltaR, realized_price ``` ```python d0 = 5 #million DAI p0 = .01 #DAI per tokens theta = .4 R0 = d0*(1-theta) #million DAI S0 = d0/p0 kappa = 6 V0 = invariant(R0,S0,kappa) reserve = np.arange(0,100,.01) supp = np.array([supply(r,kappa, V0) for r in reserve]) price = np.array([spot_price(r,kappa, V0) for r in reserve]) fig, ax1 = plt.subplots() color = 'tab:red' ax1.set_xlabel('Reserve (Millions of xDAI)') ax1.set_ylabel('Supply (Millions of Tokens)', color=color) ax1.plot(reserve, supp,'--', color=color) ax1.tick_params(axis='y', labelcolor=color) ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis color = 'tab:blue' ax2.set_ylabel('Price in xDAI per Token', color=color) # we already handled the x-label with ax1 ax2.plot(reserve, price,'-.', color=color) ax2.tick_params(axis='y', labelcolor=color) ax1.vlines(R0,0,supp[-1], alpha=.5) ax1.text(R0+.02*reserve[-1], supp[-1], "Initial Value R="+str(int(100*R0)/100)+" mil xDAI") ax1.text(R0+.02*reserve[-1], .95*supp[-1], "Initial Value S="+str(S0)+" mil Tokens") #ax1.hlines(S0,0,R0) ax2.text(R0+.02*reserve[-1], price[3], "Initial Value p1="+str(int(100*spot_price(R0,kappa,V0))/100)) plt.title('Augmented Bonding Curve with Invariant S^'+str(kappa)+'/R') fig.tight_layout() # otherwise the right y-label is slightly clipped plt.show() ``` ![](https://i.imgur.com/558HG6S.png) ```python fig, ax1 = plt.subplots() cp = 100 color = 'tab:red' ax1.set_xlabel('Supply (Millions of Tokens)') ax1.set_ylabel('Reserve (Millions of xDAI)', color=color) ax1.plot(supp[cp:], reserve[cp:],'--', color=color) ax1.tick_params(axis='y', labelcolor=color) ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis color = 'tab:blue' ax2.set_ylabel('Price in xDAI per Token', color=color) # we already handled the x-label with ax1 ax2.plot(supp[cp:], price[cp:],'-.', color=color) ax2.tick_params(axis='y', labelcolor=color) ax1.vlines(S0,0,reserve[-1], alpha=.5) ax1.text(S0*1.02, reserve[-1], "Initial Value S="+str(int(100*S0)/100)+" mil tokens") ax1.text(S0*1.02, .95*reserve[-1], "Initial Value R="+str(R0)+" mil xDAI") #ax1.hlines(S0,0,R0) ax2.text(S0*1.02, price[3], "Initial Value p1="+str(int(100*spot_price(R0,kappa,V0))/100)) plt.title('Augmented Bonding Curve with Invariant S^'+str(kappa)+'/R') fig.tight_layout() # otherwise the right y-label is slightly clipped plt.show() ``` ![](https://i.imgur.com/eggmFU6.png) ```python #given V0 and kappa #sweep the reserve reserve = None reserve = np.arange(.01,100,.01) price = np.array([spot_price(r,kappa, V0) for r in reserve]) #realized price for withdrawing burning .1% of tokens withdraw_price=[withdraw(supply(r,kappa,V0)/1000, r,supply(r,kappa,V0), kappa, V0)[1] for r in reserve] #realized price for depositing .1% more Xdai into the reserve mint_price=[mint(r/1000, r, supply(r,kappa,V0), kappa, V0)[1] for r in reserve] ``` ```python from IPython.display import Image Image(filename='slippage.jpeg') ``` ![](https://i.imgur.com/2jZGIcm.jpg) ```python pdf = pd.DataFrame({'reserve':reserve, 'spot_price':price, '.1% mint_price':mint_price,'.1% withdraw_price':withdraw_price }) ``` ```python pdf.plot(x='reserve') ``` <matplotlib.axes._subplots.AxesSubplot at 0x1a21eee160> ![](https://i.imgur.com/3wjvui6.png) ```python pdf['mint_slippage'] = (pdf['.1% mint_price']-pdf['spot_price'])/pdf['spot_price'] pdf['withdraw_slippage'] = (pdf['spot_price']-pdf['.1% withdraw_price'])/pdf['spot_price'] ``` ```python pdf.plot(x='reserve', y = ['mint_slippage', 'withdraw_slippage'])#, logy=True) ``` <matplotlib.axes._subplots.AxesSubplot at 0x1a22526358> ![](https://i.imgur.com/3gzbGT3.png) ```python #given V0 and kappa R = 20 S = supply(R,kappa,V0) p = spot_price(R,kappa,V0) #sweep the transaction fraction TXF = np.logspace(-6, 0, num=1000) #realized price for withdrawing burning .1% of tokens withdraw_price2=[withdraw(S*txf, R,S, kappa, V0)[1] for txf in TXF] #realized price for depositing .1% more Xdai into the reserve mint_price2=[mint(R*txf, R,S, kappa, V0)[1] for txf in TXF] ``` ```python print(S) ``` 685.9431568581422 ```python pdf2 = pd.DataFrame({'tx_fraction':TXF, 'spot_price':p*np.ones(len(TXF)), 'mint_price':mint_price2,'withdraw_price':withdraw_price2 }) ``` ```python pdf2.plot(x='tx_fraction',y=['mint_price','withdraw_price','spot_price'], logx=True) ``` <matplotlib.axes._subplots.AxesSubplot at 0x1a2255b780> ![](https://i.imgur.com/0ZWEfeQ.png) ```python pdf2['mint_slippage'] = (pdf2['mint_price']-pdf2['spot_price'])/pdf2['spot_price'] pdf2['withdraw_slippage'] = (pdf2['spot_price']-pdf2['withdraw_price'])/pdf2['spot_price'] ``` ```python pdf2.plot(x='tx_fraction', y = ['mint_slippage', 'withdraw_slippage'], logx=True, logy=True) ``` <matplotlib.axes._subplots.AxesSubplot at 0x1a22b0ae48> ![](https://i.imgur.com/UMYDazc.png) ```python Kappa_List = [2,4,6,8] for kappa in Kappa_List: V0 = invariant(R0,S0,kappa) reserve = np.arange(0,100,.01) supp = np.array([supply(r,kappa, V0) for r in reserve]) price = np.array([spot_price(r,kappa, V0) for r in reserve]) fig, ax1 = plt.subplots() color = 'tab:red' ax1.set_xlabel('Reserve (Millions of xDAI)') ax1.set_ylabel('Supply (Millions of Tokens)', color=color) ax1.plot(reserve, supp,'--', color=color) ax1.tick_params(axis='y', labelcolor=color) ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis color = 'tab:blue' ax2.set_ylabel('Price in xDAI per Token', color=color) # we already handled the x-label with ax1 ax2.plot(reserve, price,'-.', color=color) ax2.tick_params(axis='y', labelcolor=color) ax1.vlines(R0,0,supp[-1], alpha=.5) ax1.text(R0+.02*reserve[-1], supp[-1], "Initial Value R="+str(int(100*R0)/100)+" mil xDAI") ax1.text(R0+.02*reserve[-1], .95*supp[-1], "Initial Value S="+str(int(100*S0)/100)+" mil Tokens") #ax1.hlines(S0,0,R0) ax2.text(R0+.02*reserve[-1], price[3], "Initial Value p1="+str(int(1000*spot_price(R0,kappa,V0))/1000)) plt.title('Augmented Bonding Curve with Invariant S^'+str(kappa)+'/R') fig.tight_layout() # otherwise the right y-label is slightly clipped plt.show() ``` ![](https://i.imgur.com/iCTVp6J.png) ![](https://i.imgur.com/N0nCf2F.png) ![](https://i.imgur.com/YRRBm0E.png) ![](https://i.imgur.com/vDtjQGX.png) ```python #Power function independent variables for analysis vec_d0 = np.arange(2.5,5.1,.1) #millon dai vec_theta = np.arange(.1,.55,.05) #unitless mat_R0 = np.outer(vec_d0.T, (1-vec_theta)) #million dai vec_p0 = np.arange(.01,.11,.01) #dai per token mat_S0 = np.outer(vec_d0.T, vec_p0) #milion tokens vec_kappa = np.arange(2,9,1) #integer mat_return_ratio = np.outer(vec_kappa.T, (1-vec_theta)) ``` ```python p0_lab = [str(int(100*p)/100) for p in vec_p0] th_lab = [str(int(100*th)/100) for th in vec_theta] k_lab = [str(k) for k in vec_kappa] sns.heatmap(mat_return_ratio.T,yticklabels=th_lab, xticklabels=k_lab, annot=True) plt.yticks(rotation=0) plt.xlabel('Invariant Power: Kappa') plt.ylabel('Funding Pool Fraction: Theta') plt.title('Hatch Return Rate p1/p0') ``` Text(0.5, 1.0, 'Hatch Return Rate p1/p0') ![](https://i.imgur.com/7thgXTc.png) ```python d_lab = [str(int(100*d)/100) for d in vec_d0] sns.heatmap(vec_d0-mat_R0.T,yticklabels=th_lab, xticklabels=d_lab)#, annot=True) plt.yticks(rotation=0) plt.xlabel('Initial Fundraise: d0 (Millions of xDAI)') plt.ylabel('Funding Pool Fraction: Theta') plt.title('Funding Pool Funds at Launch (Millions of xDAI)') ``` Text(0.5, 1.0, 'Funding Pool Funds at Launch (Millions of xDAI)') ![](https://i.imgur.com/5m1y14p.png) ```python d_lab = [str(int(100*d)/100) for d in vec_d0] sns.heatmap(mat_R0.T,yticklabels=th_lab, xticklabels=d_lab)#, annot=True) plt.yticks(rotation=0) plt.title('Initial Reserve: R0 (Millions of xDAI)') plt.ylabel('Funding Pool Fraction: Theta') plt.xlabel('Intial Raise d0 (Millions of xDAI)') ``` Text(0.5, 15.0, 'Intial Raise d0 (Millions of xDAI)') ![](https://i.imgur.com/2kfKCET.png) ```python sns.heatmap(mat_S0.T,yticklabels=p0_lab, xticklabels=d_lab)#, annot=True) plt.yticks(rotation=0) plt.title('Initial Supply: S0 (Millions of Tokens)') plt.ylabel('Hatch Sale Price: p0 (xDAI per Token)') plt.xlabel('Intial Raise d0 (Millions of xDAI)') ``` Text(0.5, 15.0, 'Intial Raise d0 (Millions of xDAI)') ![](https://i.imgur.com/qD5XlYZ.png) ```python ```

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