Add new tutorial for multi-currencies arbitrage using Bellman-Ford Algorithm #48
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## Description Replying to this issue Related Issue How to extract Implied volatility from Quantconnect QuantConnect#47 Types of changes -[ ] Bug fix (non-breaking change which fixes an issue) -[ ] Refactor (non-breaking change which improves implementation) -[ ] New feature (non-breaking change which adds functionality) -[ x] Non-functional change (xml comments/documentation/etc) Checklist: -[ x] My code follows the code style of this project. -[ x] I have read the CONTRIBUTING [document](https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md). -[ x] My branch follows the naming convention bug-<issue#>-<description> or feature-<issue#>-<description>
## Description Adding nee tutorial Types of changes -[ ] Bug fix (non-breaking change which fixes an issue) -[ ] Refactor (non-breaking change which improves implementation) -[ ] New feature (non-breaking change which adds functionality) -[ x] Non-functional change (xml comments/documentation/etc) Checklist: -[ x] My code follows the code style of this project. -[ x] I have read the CONTRIBUTING [document](https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md). -[ x] My branch follows the naming convention bug-<issue#>-<description> or feature-<issue#>-<description>
Capital Structure Arbitrage ## Description Adding nee tutorial Types of changes -[ ] Bug fix (non-breaking change which fixes an issue) -[ ] Refactor (non-breaking change which improves implementation) -[ ] New feature (non-breaking change which adds functionality) -[ x] Non-functional change (xml comments/documentation/etc) Checklist: -[ x] My code follows the code style of this project. -[ x] I have read the CONTRIBUTING [document](https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md). -[ x] My branch follows the naming convention bug-<issue#>-<description> or feature-<issue#>-<description>
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Thank you for the commits! Looks great and technical. Leaving a few comments above. |
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For capital_structure_arbitrage.ipynb, it is a piece of information with math formula development right? So maybe you can change it into a |
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Hey Louis,
I really appreciate your feedback! I’ll fix in the upcoming days!
Regards,
Attilio
Il giorno mer 10 apr 2024 alle 09:37 Louis Szeto ***@***.***>
ha scritto:
… For capital_structure_arbitrage.ipynb
<https://github.com/QuantConnect/Research/pull/48/files#diff-08b49dd48eccf2d3c59f3964a4d76f594d1a630a7c0065c885e8a133e00c5e1a>,
it is a piece of information with math formula development right? So maybe
you can change it into a .md file with latex to type in the formula.
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Hey,
capital_structure_arbitrage.ipynb
<https://github.com/QuantConnect/Research/pull/48/files#diff-08b49dd48eccf2d3c59f3964a4d76f594d1a630a7c0065c885e8a133e00c5e1a>
is
actually a notebook with python code and math formula, so it's not possible
to make a single .md file.
Regards,
Attilio
Il giorno mer 10 apr 2024 alle ore 09:37 Louis Szeto <
***@***.***> ha scritto:
… For capital_structure_arbitrage.ipynb
<https://github.com/QuantConnect/Research/pull/48/files#diff-08b49dd48eccf2d3c59f3964a4d76f594d1a630a7c0065c885e8a133e00c5e1a>,
it is a piece of information with math formula development right? So maybe
you can change it into a .md file with latex to type in the formula.
—
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<#48 (comment)>,
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Hey Louis,
what's the correct folder?
Regards,
Attilio
Il giorno mer 10 apr 2024 alle ore 09:35 Louis Szeto <
***@***.***> ha scritto:
… Thank you for the commits! Looks great and technical. Leaving a few
comments above.
BTW I think Local_Stochastic_Volatility_Model.ipynb
<https://github.com/QuantConnect/Research/pull/48/files/33eaadb7717742e7ad3df123d561c0be4ea85900#diff-b621b661217f0674a1c2e11a56f70a8b1465dc8e2a4e4fc63e4b4528f462a109>
is in a wrong location. Please put it into the folder like the others.
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@leo-ai-for-trading Local_Stochastic_Volatility_Model.ipynb This notebook should be placed in the same folder as the others. |
LouisSzeto
requested changes
Apr 16, 2024
| " price2 = qb.History(ticker2, start_time, end_time, Resolution.Tick)['lastprice']\n", | ||
| " price3 = qb.History(ticker3, start_time, end_time, Resolution.Tick)['lastprice']\n", | ||
| "\n", | ||
| " df = pd.DataFrame({'symbol':list(),'price':list()})\n", |
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May add a comment on why using negative log price (for minimum distance and eliminate compounding effect...)
| " @start_time: date to start getting data\n", | ||
| " @end_time: last day of getting data\n", | ||
| " \"\"\"\n", | ||
| "\n", |
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I think there is some restrictions on how do you choose the crypto. Say if we choose BTCUSD, ETHUSD & BTCETH, there will be 4 currencies and 3 directional edges that always get positive results. If we're doing arbitrage, we should do BTCUSD, USDETH & ETHBTC to force a long-short balance in each currency. It will be good to add description above.
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| " price1 = qb.History(ticker1, start_time, end_time, Resolution.Tick)['lastprice']\n", | ||
| " price2 = qb.History(ticker2, start_time, end_time, Resolution.Tick)['lastprice']\n", | ||
| " price3 = qb.History(ticker3, start_time, end_time, Resolution.Tick)['lastprice']\n", |
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Tick resolution might not align with time, recommend to use second instead. Also, there might not be trading information some illiquid currencies, it maybe better to use History[QuoteBar].
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| "\\begin{aligned}\n", | ||
| "&\\begin{aligned}\n", | ||
| "\\sigma_{l o c}^2(K, T, S) & =\\sigma^2-\\lambda \\frac{K \\frac{\\partial C}{\\partial K}}{\\frac{1}{2} K^2 \\frac{\\partial^2 C}{\\partial K^2}} \\\\\n", | ||
| "& =\\sigma^2+2 \\lambda \\sigma \\sqrt{T} \\frac{N\\left(d_2\\right)}{N^{\\prime}\\left(d_2\\right)}\n", | ||
| "\\end{aligned}\\\\\n", | ||
| "&d_2=\\frac{\\log S / K+\\lambda T}{\\sigma \\sqrt{T}}-\\frac{\\sigma \\sqrt{T}}{2}\n", | ||
| "\\end{aligned}" |
| "source": [ | ||
| "\n", | ||
| " \n", | ||
| "def get_data(sym1:str,sym2:str, sym3:str , start_time: datetime, end_time: datetime):\n", |
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nit - we use (self, arg1: type1 = a, arg2: type2 = b, arg3: type3 = c) -> type4:. Please fix the nit
| " def __getitem__(self,v):\n", | ||
| " return self.neighbors[v]\n", | ||
| " \n", | ||
| " def add_node(self,name):\n", |
| " self.weight.append({})\n", | ||
| " return self.name2node[name]\n", | ||
| " \n", | ||
| " def add_edge(self,name_u,name_v,weight_uv=None):\n", |
| " self.add_arc(name_u, name_v, weight_uv) \n", | ||
| " self.add_arc(name_v, name_u, weight_uv)\n", | ||
| "\n", | ||
| " def add_arc(self,name_u,name_v,weight_uv=None):\n", |
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| "r = 0.0341 \n", | ||
| "D = 0.0\n", |
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Can use Lean's InterestRateProvider
InterestRateProvider().GetInterestRate(goog_df.index[...]) # sub in the time
DividendYieldProvider(goog).GetDividendYield(goog_df.index[...]) # sub in the time
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Description
Adding docs for the calculation of local volatility model
Related Issue
N/A
Motivation and Context
tutorial for new members (in particular with heavy Math background)
Types of changes
Checklist: