Comparing Outcomes

Comparing Outcomes

Comparing Outcomes

Jen Albert

Jen Albert

Jen Albert

Jan 3, 2023

Jan 3, 2023

Jan 3, 2023

constant product concentrated liquidity asymmetric liquidity uniswap Carbon DeFi trading decentralized finance trading bot range trading DEX
constant product concentrated liquidity asymmetric liquidity uniswap Carbon DeFi trading decentralized finance trading bot range trading DEX
constant product concentrated liquidity asymmetric liquidity uniswap Carbon DeFi trading decentralized finance trading bot range trading DEX

Carbon Strategies, Constant Product, and HODL


The most recent Bancor Community Call included a slideshow presentation. A simulation was performed to compare a generic Carbon strategy versus a traditional constant product AMM, both benchmarked against HODL. The simulation is relatively simple; a mock price chart with 200 price points was used as a reference, from which arbitrage trades could be computed, and executed. In the constant product case, arbitrage was simulated such that the marginal price was met exactly during each trade, which “increases volume and gives the constant product version the best possible fighting chance of beating the asymmetric one” (Carbon strategy). The standard 0.3% fee used by Uni V2 was used throughout.


Alice, Bob, and Charlie all start with 100 TKN and 1000 USDC. Due to the 50/50 requirement when providing liquidity to a constant product AMM, TKN in this example is valued at $10 USDC per TKN. Therefore, each user has $1000 worth of each token, and $2000 total value.


Alice is creating a ‘buy low, sell high’ Carbon strategy made of two individual range orders that automatically link together. TKN is currently valued at $10 USD per TKN and Alice chooses to set the parameters for her buy order at $9–8.5 USDC per TKN. For her sell order, Alice sets her parameters at $11.50–13 USDC per TKN. Alice’s USDC and TKN are split evenly and contributed to this strategy using two individual bonding curves, one for each order.


In the graphic below, Alice’s buy and sell zones are represented by the green and red rectangles, respectively. The simulated price action is represented by an orange line chart. When the chart first hits the sell zone, the TKN liquidity becomes active. Although the price action just barely reached Alice’s target, it is assumed that market participants are interacting with her position due to the length of time it remained there. As Alice’s TKN is being sold according to her first order, her USDC balance is increasing in her second order. She now has the ability to buy more TKN should it capitulate down into her buy zone, which it does shortly thereafter. Between 100 and 125 on the time axis, Alice is buying the proverbial ‘dip’ with the USDC garnered from the sale of her TKN earlier, between 25 and 75 on the time axis. There are no alerts or new orders being created; the protocol is automatically executing Alice’s strategy without requiring her attention.


As shown in the picture below, the chart continues to move up and back into Alice’s sell zone. When the price action first re-enters Alice’s target range, some of the new TKN acquired at the dip is now sold off. Once the price exceeds Alice’s sell zone all of her TKN has been converted into USDC, which concludes the simulation.


Alice now has a few options:

  1. Continue to let the strategy play out if she thinks the price will re-enter her buy zone.


  2. Cancel the strategy and create a new one using her USDC and another TKN.


  3. Adjust her parameters to reflect the changing market conditions.


  4. HODL her USDC for a future trading opportunity.



Charlie chose not to interact with any DeFi protocols and to keep his $1000 USDC and 100 TKN in his wallet. His strategy is to HODL. At the end of the example, TKN is valued at $13 USD per TKN, valuing his total 100 TKN at $1300 USD. Charlie also still has his original $1,000 USDC. The value of his portfolio went from $2,000 USD to $2,300 USD. His strategy was proven effective, resulting in a $300 overall increase in his portfolio valuation.


Bob provided liquidity on a traditional constant product AMM. He had an equal value of USDC and TKN, and he contributed the entire $2,000 to the liquidity pool. Bob’s strategy also resulted in a net profit, but it proved inferior to Charlie’s HODL strategy (see below). Charlie HODL’d and now has a portfolio valued at $2,300, whereas Bob provided liquidity and now has a portfolio value of $2,290. While Bob profited $290, he is still $10 short compared to HODL. This result is conventionally referred to as impermanent loss, one of the major struggles of providing liquidity to a constant product AMM.



Alice, using the asymmetric liquidity strategy on Carbon, benefitted significantly more than if she had chosen to HODL. According to the results of the simulation, at the end of her strategy, Alice was left with a portfolio balance of $2,732, totaling a profit of $732, over 2.5x the results Bob saw after providing liquidity.


Key Take-Aways

Charlie’s HODL strategy resulted in a $300 USD profit, the second highest in the simulation. Bob’s approach in providing liquidity to a constant product AMM proved to be the least profitable of the three options presented. He obtained a total profit of $290 USDC — a $10 loss when compared to Charlie’s HODL position. Alice’s approach was asymmetric. Her strategy on Carbon proved to be the most successful with a profit of $732 USD.


Alice’s strategy was created and executed by only taking a few simple steps:

  1. Nominated buy and sell orders with custom ranges.


  2. These orders are automatically linked together, and comprise her strategy.


  3. Fund one or both orders with the ERC-20 token(s) of choice.


  4. As an order is filled (partially or completely), liquidity continuously rotates to its counterpart, automatically funding the execution of the strategy.


  5. Set the strategy live.


That’s it! All other steps were automatically executed by the protocol according to the parameters chosen by Alice.

Carbon Strategies, Constant Product, and HODL


The most recent Bancor Community Call included a slideshow presentation. A simulation was performed to compare a generic Carbon strategy versus a traditional constant product AMM, both benchmarked against HODL. The simulation is relatively simple; a mock price chart with 200 price points was used as a reference, from which arbitrage trades could be computed, and executed. In the constant product case, arbitrage was simulated such that the marginal price was met exactly during each trade, which “increases volume and gives the constant product version the best possible fighting chance of beating the asymmetric one” (Carbon strategy). The standard 0.3% fee used by Uni V2 was used throughout.


Alice, Bob, and Charlie all start with 100 TKN and 1000 USDC. Due to the 50/50 requirement when providing liquidity to a constant product AMM, TKN in this example is valued at $10 USDC per TKN. Therefore, each user has $1000 worth of each token, and $2000 total value.


Alice is creating a ‘buy low, sell high’ Carbon strategy made of two individual range orders that automatically link together. TKN is currently valued at $10 USD per TKN and Alice chooses to set the parameters for her buy order at $9–8.5 USDC per TKN. For her sell order, Alice sets her parameters at $11.50–13 USDC per TKN. Alice’s USDC and TKN are split evenly and contributed to this strategy using two individual bonding curves, one for each order.


In the graphic below, Alice’s buy and sell zones are represented by the green and red rectangles, respectively. The simulated price action is represented by an orange line chart. When the chart first hits the sell zone, the TKN liquidity becomes active. Although the price action just barely reached Alice’s target, it is assumed that market participants are interacting with her position due to the length of time it remained there. As Alice’s TKN is being sold according to her first order, her USDC balance is increasing in her second order. She now has the ability to buy more TKN should it capitulate down into her buy zone, which it does shortly thereafter. Between 100 and 125 on the time axis, Alice is buying the proverbial ‘dip’ with the USDC garnered from the sale of her TKN earlier, between 25 and 75 on the time axis. There are no alerts or new orders being created; the protocol is automatically executing Alice’s strategy without requiring her attention.


As shown in the picture below, the chart continues to move up and back into Alice’s sell zone. When the price action first re-enters Alice’s target range, some of the new TKN acquired at the dip is now sold off. Once the price exceeds Alice’s sell zone all of her TKN has been converted into USDC, which concludes the simulation.


Alice now has a few options:

  1. Continue to let the strategy play out if she thinks the price will re-enter her buy zone.


  2. Cancel the strategy and create a new one using her USDC and another TKN.


  3. Adjust her parameters to reflect the changing market conditions.


  4. HODL her USDC for a future trading opportunity.



Charlie chose not to interact with any DeFi protocols and to keep his $1000 USDC and 100 TKN in his wallet. His strategy is to HODL. At the end of the example, TKN is valued at $13 USD per TKN, valuing his total 100 TKN at $1300 USD. Charlie also still has his original $1,000 USDC. The value of his portfolio went from $2,000 USD to $2,300 USD. His strategy was proven effective, resulting in a $300 overall increase in his portfolio valuation.


Bob provided liquidity on a traditional constant product AMM. He had an equal value of USDC and TKN, and he contributed the entire $2,000 to the liquidity pool. Bob’s strategy also resulted in a net profit, but it proved inferior to Charlie’s HODL strategy (see below). Charlie HODL’d and now has a portfolio valued at $2,300, whereas Bob provided liquidity and now has a portfolio value of $2,290. While Bob profited $290, he is still $10 short compared to HODL. This result is conventionally referred to as impermanent loss, one of the major struggles of providing liquidity to a constant product AMM.



Alice, using the asymmetric liquidity strategy on Carbon, benefitted significantly more than if she had chosen to HODL. According to the results of the simulation, at the end of her strategy, Alice was left with a portfolio balance of $2,732, totaling a profit of $732, over 2.5x the results Bob saw after providing liquidity.


Key Take-Aways

Charlie’s HODL strategy resulted in a $300 USD profit, the second highest in the simulation. Bob’s approach in providing liquidity to a constant product AMM proved to be the least profitable of the three options presented. He obtained a total profit of $290 USDC — a $10 loss when compared to Charlie’s HODL position. Alice’s approach was asymmetric. Her strategy on Carbon proved to be the most successful with a profit of $732 USD.


Alice’s strategy was created and executed by only taking a few simple steps:

  1. Nominated buy and sell orders with custom ranges.


  2. These orders are automatically linked together, and comprise her strategy.


  3. Fund one or both orders with the ERC-20 token(s) of choice.


  4. As an order is filled (partially or completely), liquidity continuously rotates to its counterpart, automatically funding the execution of the strategy.


  5. Set the strategy live.


That’s it! All other steps were automatically executed by the protocol according to the parameters chosen by Alice.

Carbon Strategies, Constant Product, and HODL


The most recent Bancor Community Call included a slideshow presentation. A simulation was performed to compare a generic Carbon strategy versus a traditional constant product AMM, both benchmarked against HODL. The simulation is relatively simple; a mock price chart with 200 price points was used as a reference, from which arbitrage trades could be computed, and executed. In the constant product case, arbitrage was simulated such that the marginal price was met exactly during each trade, which “increases volume and gives the constant product version the best possible fighting chance of beating the asymmetric one” (Carbon strategy). The standard 0.3% fee used by Uni V2 was used throughout.


Alice, Bob, and Charlie all start with 100 TKN and 1000 USDC. Due to the 50/50 requirement when providing liquidity to a constant product AMM, TKN in this example is valued at $10 USDC per TKN. Therefore, each user has $1000 worth of each token, and $2000 total value.


Alice is creating a ‘buy low, sell high’ Carbon strategy made of two individual range orders that automatically link together. TKN is currently valued at $10 USD per TKN and Alice chooses to set the parameters for her buy order at $9–8.5 USDC per TKN. For her sell order, Alice sets her parameters at $11.50–13 USDC per TKN. Alice’s USDC and TKN are split evenly and contributed to this strategy using two individual bonding curves, one for each order.


In the graphic below, Alice’s buy and sell zones are represented by the green and red rectangles, respectively. The simulated price action is represented by an orange line chart. When the chart first hits the sell zone, the TKN liquidity becomes active. Although the price action just barely reached Alice’s target, it is assumed that market participants are interacting with her position due to the length of time it remained there. As Alice’s TKN is being sold according to her first order, her USDC balance is increasing in her second order. She now has the ability to buy more TKN should it capitulate down into her buy zone, which it does shortly thereafter. Between 100 and 125 on the time axis, Alice is buying the proverbial ‘dip’ with the USDC garnered from the sale of her TKN earlier, between 25 and 75 on the time axis. There are no alerts or new orders being created; the protocol is automatically executing Alice’s strategy without requiring her attention.


As shown in the picture below, the chart continues to move up and back into Alice’s sell zone. When the price action first re-enters Alice’s target range, some of the new TKN acquired at the dip is now sold off. Once the price exceeds Alice’s sell zone all of her TKN has been converted into USDC, which concludes the simulation.


Alice now has a few options:

  1. Continue to let the strategy play out if she thinks the price will re-enter her buy zone.


  2. Cancel the strategy and create a new one using her USDC and another TKN.


  3. Adjust her parameters to reflect the changing market conditions.


  4. HODL her USDC for a future trading opportunity.



Charlie chose not to interact with any DeFi protocols and to keep his $1000 USDC and 100 TKN in his wallet. His strategy is to HODL. At the end of the example, TKN is valued at $13 USD per TKN, valuing his total 100 TKN at $1300 USD. Charlie also still has his original $1,000 USDC. The value of his portfolio went from $2,000 USD to $2,300 USD. His strategy was proven effective, resulting in a $300 overall increase in his portfolio valuation.


Bob provided liquidity on a traditional constant product AMM. He had an equal value of USDC and TKN, and he contributed the entire $2,000 to the liquidity pool. Bob’s strategy also resulted in a net profit, but it proved inferior to Charlie’s HODL strategy (see below). Charlie HODL’d and now has a portfolio valued at $2,300, whereas Bob provided liquidity and now has a portfolio value of $2,290. While Bob profited $290, he is still $10 short compared to HODL. This result is conventionally referred to as impermanent loss, one of the major struggles of providing liquidity to a constant product AMM.



Alice, using the asymmetric liquidity strategy on Carbon, benefitted significantly more than if she had chosen to HODL. According to the results of the simulation, at the end of her strategy, Alice was left with a portfolio balance of $2,732, totaling a profit of $732, over 2.5x the results Bob saw after providing liquidity.


Key Take-Aways

Charlie’s HODL strategy resulted in a $300 USD profit, the second highest in the simulation. Bob’s approach in providing liquidity to a constant product AMM proved to be the least profitable of the three options presented. He obtained a total profit of $290 USDC — a $10 loss when compared to Charlie’s HODL position. Alice’s approach was asymmetric. Her strategy on Carbon proved to be the most successful with a profit of $732 USD.


Alice’s strategy was created and executed by only taking a few simple steps:

  1. Nominated buy and sell orders with custom ranges.


  2. These orders are automatically linked together, and comprise her strategy.


  3. Fund one or both orders with the ERC-20 token(s) of choice.


  4. As an order is filled (partially or completely), liquidity continuously rotates to its counterpart, automatically funding the execution of the strategy.


  5. Set the strategy live.


That’s it! All other steps were automatically executed by the protocol according to the parameters chosen by Alice.

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