From 1b3380e29a9623ccb493c3a611c054e44b535562 Mon Sep 17 00:00:00 2001 From: Meek Msaki <98189596+mmsaki@users.noreply.github.com> Date: Sat, 16 Nov 2024 04:33:19 -0600 Subject: [PATCH] =?UTF-8?q?=E2=9A=A1=EF=B8=8F=20uv=20lint=20rules=20and=20?= =?UTF-8?q?docummentation?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 53 +- docs/notebooks/graph.ipynb | 339 +++---- pyproject.toml | 19 +- requirements.txt | 157 ---- src/defigraph/Edge.py | 36 +- src/defigraph/Graph.py | 155 ++-- src/defigraph/Pool.py | 60 +- src/defigraph/Vertex.py | 43 +- tests/Edge_test.py | 6 +- tests/Graph_test.py | 6 +- tests/Pool_test.py | 116 ++- tests/Vertex_test.py | 73 +- uv.lock | 1749 ++++++++++++++++++++++++++++++++++++ 13 files changed, 2245 insertions(+), 567 deletions(-) delete mode 100644 requirements.txt create mode 100644 uv.lock diff --git a/README.md b/README.md index 90f871d..1d40909 100644 --- a/README.md +++ b/README.md @@ -1,65 +1,74 @@ # Defi Graph +![GitHub](https://img.shields.io/github/license/mmsaki/defigraph) +![GitHub repo size](https://img.shields.io/github/repo-size/mmsaki/defigraph) +![GitHub last commit](https://img.shields.io/github/last-commit/mmsaki/defigraph) +![PyPI - Version](https://img.shields.io/pypi/v/defigraph) +![PyPI - Downloads](https://img.shields.io/pypi/dm/defigraph) +![GitHub top language](https://img.shields.io/github/languages/top/mmsaki/defigraph) + +![X (formerly Twitter) Follow](https://img.shields.io/twitter/follow/msakiart) + This is package for creating and visualizing graphs in DeFi protocols. ## Modules -- [x] [Edge](src/defigraph/Edge.py) - describes a path bewtween tokens -- [x] [Vertex](src/defigraph/Vertex.py) - describes a token -- [x] [Graph](src/defigraph/Graph.py) - describes a defi market -- [x] [Pool](src/defigraph/Pool.py) - describes trading token pairs +- [x] [Edge](#edge-type) - describes a path between tokens +- [x] [Vertex](#vertex-type) - describes a token +- [x] [Graph](#graph-type) - describes a defi market +- [x] [Pool](#pool-type) - describes trading token pairs ## Edge type Edges store two main state: 1. `self.weight` - a weight between two tokens - - typeof `float` - - `{0,1}` Depends on direction of edge - - Calculated as `-Math.log(self.pool.token_price_{0,1})` + - typeof `float` + - `{0,1}` Depends on direction of edge + - Calculated as `-Math.log(self.pool.token_price_{0,1})` 1. `self.pool` - an instance of a token pool - - typeof `Pool` + - typeof `Pool` 1. `self._tuple` - Allows indexing/iteration of the edge object - - typeof `iter` - - Array of `[Vertex1, Vertex2, (self.weight, Pool)]` + - typeof `iter` + - Array of `[Vertex1, Vertex2, (self.weight, Pool)]` ## Pool type A pool object describing tokens: 1. `self.address` - the address of the pool - - typeof `Hex` checksum address + - typeof `Hex` checksum address 1. `self.token0` - a token described as a vertex - - typeof `Vertex` + - typeof `Vertex` 1. `self.token1` - a token described as a vertex - - typeof `Vertex` + - typeof `Vertex` 1. `self.token0_price` - the price of token0 - - typeof `float` + - typeof `float` 1. `self.token1_price` - the price of token1 - - typeof `float + - typeof `float 1. `self.fee` - describes the pool fee e.g Uniswap (100 | 500 | 1000 | 3000) - - typeof `int` + - typeof `int` ## Graph type An adjacency list graph object desribing a defi market 1. `self.vertices` - contains a list of all vertices - - typeof `List[Vertex]` + - typeof `List[Vertex]` 1. `self.adjascency_list` - a mapping describing edges in the graph - - typeof `Dict[Vertex, List[Edge]]` - - example: {Vertex: [Edge1, Edge2, Edge3, Edge4]} + - typeof `Dict[Vertex, List[Edge]]` + - example: {Vertex: [Edge1, Edge2, Edge3, Edge4]} ## Vertex type A node on the graph describing a token 1. `self.name` - name of the token - - typeof `string` + - typeof `string` 1. `self.decimals` - number of decimals for token - - typeof `int` + - typeof `int` 1. `self.address` - address of token - - typeof `Hex` checksum address + - typeof `Hex` checksum address ## Tests diff --git a/docs/notebooks/graph.ipynb b/docs/notebooks/graph.ipynb index ad9d99a..536ca4a 100644 --- a/docs/notebooks/graph.ipynb +++ b/docs/notebooks/graph.ipynb @@ -13,7 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "# import modules: `pip install defigraph` or `python -m pip install -e .` \n", + "# import modules: `pip install defigraph` or `python -m pip install -e .`\n", "from defigraph.Graph import Graph\n", "from defigraph.Edge import Edge\n", "from defigraph.Vertex import Vertex\n", @@ -36,13 +36,13 @@ "# Option 1: Use data from cached results\n", "import json\n", "\n", - "with open('data.json') as f:\n", - " data = json.load(f)" + "with open(\"data.json\") as f:\n", + " data = json.load(f)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -55,21 +55,20 @@ "\n", "api_key = os.getenv(\"api_key\")\n", "base_url = \"https://gateway.thegraph.com/api\"\n", - "# ETH-mainnet \n", - "subgraph_id = \"5zvR82QoaXYFyDEKLZ9t6v9adgnptxYpKpSbxtgVENFV\" # Uniswap V3\n", + "# ETH-mainnet\n", + "subgraph_id = \"5zvR82QoaXYFyDEKLZ9t6v9adgnptxYpKpSbxtgVENFV\" # Uniswap V3\n", "subgraph_url = f\"{base_url}/subgraphs/id/{subgraph_id}\"\n", - "headers = {\n", - " 'Content-Type': 'application/json',\n", - " 'Authorization': f'Bearer {api_key}'\n", - "}\n", - "payload = {\"query\": \"{ pools (orderBy: volumeUSD, orderDirection: desc, first: 5) { token0 { name symbol decimals id } token0Price token1Price token1 { decimals name symbol id } feeTier id } }\"} # V3\n", + "headers = {\"Content-Type\": \"application/json\", \"Authorization\": f\"Bearer {api_key}\"}\n", + "payload = {\n", + " \"query\": \"{ pools (orderBy: volumeUSD, orderDirection: desc, first: 50) { token0 { name symbol decimals id } token0Price token1Price token1 { decimals name symbol id } feeTier id } }\"\n", + "} # V3\n", "response = requests.post(subgraph_url, headers=headers, json=payload)\n", "\n", "# Check if the request was successful\n", "if response.status_code == 200:\n", - " data = response.json()['data']['pools']\n", + " data = response.json()[\"data\"][\"pools\"]\n", "else:\n", - " print(\"Error:\", response.text)" + " print(\"Error:\", response.text)" ] }, { @@ -81,52 +80,67 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "from web3 import Web3\n", + "\n", "edges = []\n", "\n", "for pool in data:\n", - " fee = pool['feeTier'] \n", + " fee = pool[\"feeTier\"]\n", "\n", - " token0 = pool['token0']['symbol']\n", - " token0_decimals = int(pool['token0']['decimals'])\n", - " token0_address = Web3.to_checksum_address(pool['token0']['id'])\n", - " token0Price = float(pool['token0Price'])\n", + " token0 = pool[\"token0\"][\"symbol\"]\n", + " token0_decimals = int(pool[\"token0\"][\"decimals\"])\n", + " token0_address = Web3.to_checksum_address(pool[\"token0\"][\"id\"])\n", + " token0Price = float(pool[\"token0Price\"])\n", "\n", - " token1 = pool['token1']['symbol']\n", - " token1_decimals = int(pool['token1']['decimals'])\n", - " token1_address = Web3.to_checksum_address(pool['token1']['id'])\n", - " token1Price = float(pool['token1Price'])\n", + " token1 = pool[\"token1\"][\"symbol\"]\n", + " token1_decimals = int(pool[\"token1\"][\"decimals\"])\n", + " token1_address = Web3.to_checksum_address(pool[\"token1\"][\"id\"])\n", + " token1Price = float(pool[\"token1Price\"])\n", "\n", - " pool_address = Web3.to_checksum_address(pool['id'])\n", + " pool_address = Web3.to_checksum_address(pool[\"id\"])\n", "\n", - " u = Vertex(token0, token0_decimals, token0_address)\n", - " v = Vertex(token1, token1_decimals, token1_address)\n", + " u = Vertex(token0, token0_decimals, token0_address)\n", + " v = Vertex(token1, token1_decimals, token1_address)\n", "\n", - " pool = Pool(pool_address=pool_address, token0=u, token1=v, fee=fee, token0_price=token0Price, token1_price=token1Price)\n", - " pool2 = Pool(pool_address=pool_address, token0=v, token1=u, fee=fee, token0_price=token1Price, token1_price=token0Price)\n", - " edge = Edge(pool=pool)\n", - " edge2 = Edge(pool=pool2)\n", - " edges.append(edge)\n", - " edges.append(edge2)\n", + " pool = Pool(\n", + " pool_address=pool_address,\n", + " token0=u,\n", + " token1=v,\n", + " fee=fee,\n", + " token0_price=token0Price,\n", + " token1_price=token1Price,\n", + " )\n", + " pool2 = Pool(\n", + " pool_address=pool_address,\n", + " token0=v,\n", + " token1=u,\n", + " fee=fee,\n", + " token0_price=token1Price,\n", + " token1_price=token0Price,\n", + " )\n", + " edge = Edge(pool=pool)\n", + " edge2 = Edge(pool=pool2)\n", + " edges.append(edge)\n", + " edges.append(edge2)\n", "\n", "g = Graph(edges=edges)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No. of vertices 4\n", - "No. of edges 10\n" + "No. of vertices 30\n", + "No. of edges 100\n" ] } ], @@ -144,108 +158,129 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "🌟 CYCLE FOUND (-7.832425629907495, (USDC, WETH, '500'))\n", - "🌟 CYCLE FOUND (0.0009050532828726206, (USDC, USDT, '100'))\n" + "🌟 CYCLE FOUND (0.005096959752283212, (USDC, UST, '100'))\n", + "UST -> USDC -> UST\n", + "Pools: [((UST, USDC, '500'), '0x92995D179a5528334356cB4Dc5c6cbb1c068696C'), ((USDC, UST, '100'), '0x18D96B617a3e5C42a2Ada4bC5d1B48e223f17D0D')]\n", + "Profit: 22.541188275053788 = 0.005096959752283212 + -3.1204411854771723\n", + "\n", + "🌟 CYCLE FOUND (14.165996771154887, (WETH, HEX, '3000'))\n", + "HEX -> WETH -> HEX\n", + "Pools: [((HEX, WETH, '3000'), '0x9e0905249CeEFfFB9605E034b534544684A58BE6'), ((WETH, HEX, '3000'), '0x9e0905249CeEFfFB9605E034b534544684A58BE6')]\n", + "Profit: 1.0 = 14.165996771154887 + -14.165996771154887\n", + "\n", + "🌟 CYCLE FOUND (-0.0006230051948524725, (USDT, DAI, '500'))\n", + "DAI -> USDT -> DAI\n", + "Pools: [((DAI, USDT, '100'), '0x48DA0965ab2d2cbf1C17C09cFB5Cbe67Ad5B1406'), ((USDT, DAI, '500'), '0x6f48ECa74B38d2936B02ab603FF4e36A6C0E3A77')]\n", + "Profit: 1.000140638971197 = -0.0006230051948524725 + 0.0004823761123883773\n", + "\n", + "[((UST, USDC, '500'), '0x92995D179a5528334356cB4Dc5c6cbb1c068696C'), ((USDC, UST, '100'), '0x18D96B617a3e5C42a2Ada4bC5d1B48e223f17D0D')]\n", + "[((HEX, WETH, '3000'), '0x9e0905249CeEFfFB9605E034b534544684A58BE6'), ((WETH, HEX, '3000'), '0x9e0905249CeEFfFB9605E034b534544684A58BE6')]\n", + "[((DAI, USDT, '100'), '0x48DA0965ab2d2cbf1C17C09cFB5Cbe67Ad5B1406'), ((USDT, DAI, '500'), '0x6f48ECa74B38d2936B02ab603FF4e36A6C0E3A77')]\n" ] - }, - { - "data": { - "text/plain": [ - "({USDC: 7.8291289941894515,\n", - " WETH: -0.002197757145362189,\n", - " USDT: 7.8310044497329905,\n", - " WBTC: -3.2745471883025288},\n", - " {USDC: (WETH, (7.831326751334814, (WETH, USDC, '3000'))),\n", - " WETH: (USDC, (-7.832425629907495, (USDC, WETH, '500'))),\n", - " USDT: (WETH, (7.833202206878353, (WETH, USDT, '500'))),\n", - " WBTC: (WETH, (-3.2723494311571666, (WETH, WBTC, '500')))})" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ + "import math\n", + "\n", + "all_neg_cycles = []\n", + "\n", "\n", "def bellman_ford(graph: Graph, start_vertex: Vertex):\n", - " \n", - " # 1. Initialize distances\n", - " distances = {key: float('inf') for key in graph.vertices}\n", - " predecessor = {key: None for key in graph.vertices}\n", - " distances[start_vertex] = 0\n", + " # 1. Initialize distances\n", + " distances = {key: float(\"inf\") for key in graph.vertices}\n", + " predecessor = {key: None for key in graph.vertices}\n", + " distances[start_vertex] = 0\n", + "\n", + " # 2. Relax edges for n - 1 steps\n", + " for i in range(len(graph.vertices) - 1):\n", + " for edge in g.get_edges():\n", + " (u, v, w) = edge\n", + " if distances[u] + w[0] < distances[v]:\n", + " distances[v] = distances[u] + w[0]\n", + " predecessor[v] = (u, w)\n", "\n", - " # 2. Relax edges for n - 1 steps\n", - " for i in range(len(graph.vertices) - 1):\n", + " # 3. Detect negative cycle\n", " for edge in g.get_edges():\n", - " (u,v,w) = edge\n", - " if distances[u] + w[0] < distances[v]:\n", - " distances[v] = distances[u] + w[0]\n", - " predecessor[v] = (u, w) \n", + " (u, v, w) = edge\n", "\n", - " # 3. Detect negative cycle\n", - " for edge in g.get_edges():\n", - " (u,v,w) = edge\n", - " if distances[u] + w[0] < distances[v]:\n", - " print(\"🌟 CYCLE FOUND\", w)\n", + " if distances[u] + w[0] < distances[v]:\n", + " cycle = []\n", + " # 1. add last token pool to cycle\n", + " # 2. add 2nd last token pool to cycle\n", + " cycle.append((w[1], w[1].address))\n", + " cycle.append((predecessor[u][1][1], predecessor[u][1][1].address))\n", "\n", + " # 1. use last token as start\n", + " # 2. initialize path tracing\n", + " # 3. initialize weight calcualtion\n", + " # 4. start with u token predecessor\n", + " start = v\n", + " s = f\"{v} <- {u} <- \"\n", + " c = f\"{str(w[0])} + {str(predecessor[u][1][0])}\"\n", + " curr = predecessor[u][0]\n", "\n", - " return distances, predecessor\n", - "bellman_ford(g, Vertex(\"WETH\", 18, Web3.to_checksum_address(\"0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2\")))\n" + " visited = set()\n", + " visited.add(w)\n", + " visited.add(predecessor[u][1])\n", + " while curr != start:\n", + " if predecessor[curr][1] in visited:\n", + " ###### ignoring cycles whose edge we have visted #####\n", + " cycle = []\n", + " break\n", + " visited.add(predecessor[curr][1])\n", + " # 1. add next path trace\n", + " # 2. add next cycle weight\n", + " # 3. add next token pool to path\n", + " # 4. update next token\n", + " s += f\"{curr} <- \"\n", + " c += f\" + {str(predecessor[curr][1][0])}\"\n", + " cycle.append((predecessor[curr][1][1], predecessor[curr][1][1].address))\n", + " curr = predecessor[curr][0]\n", + "\n", + " s += f\"{curr}\"\n", + " if cycle:\n", + " print(\"🌟 CYCLE FOUND\", w)\n", + " print(\" -> \".join(s.split(\" <- \")[::-1]))\n", + " print(f\"Pools: {cycle[::-1]}\")\n", + " print(f\"Profit: {math.e ** -eval(c)} = {c}\\n\")\n", + " all_neg_cycles.append(cycle[::-1])\n", + " # break\n", + " return distances, predecessor\n", + "\n", + "\n", + "bellman_ford(\n", + " g,\n", + " Vertex(\n", + " \"WETH\",\n", + " 18,\n", + " Web3.to_checksum_address(\"0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2\"),\n", + " ),\n", + ")\n", + "for i in all_neg_cycles:\n", + " print(i)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0022229037122259" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "import math\n", "math.e ** -(-7.830020249256383 + 0.0009123075118552688 + 7.826887505027506)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[([USDC, WETH, (-7.832425629907495, (USDC, WETH, '500'))]), ([WETH, USDC, (7.832425629907495, (WETH, USDC, '500'))]), ([WETH, USDT, (7.833202206878353, (WETH, USDT, '500'))]), ([USDT, WETH, (-7.833202206878353, (USDT, WETH, '500'))]), ([USDC, USDT, (0.0009050532828726206, (USDC, USDT, '100'))]), ([USDT, USDC, (-0.0009050532828727188, (USDT, USDC, '100'))]), ([USDC, WETH, (-7.831326751334814, (USDC, WETH, '3000'))]), ([WETH, USDC, (7.831326751334814, (WETH, USDC, '3000'))]), ([WBTC, WETH, (3.2723494311571666, (WBTC, WETH, '500'))]), ([WETH, WBTC, (-3.2723494311571666, (WETH, WBTC, '500'))])]\n", - "[USDC, WETH, USDT, WBTC]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import networkx as nx\n", "import matplotlib.pyplot as plt\n", @@ -261,9 +296,28 @@ "\n", "nx.draw_networkx_nodes(G, pos, node_color=\"tab:blue\", node_size=800)\n", "connectionstyle = [f\"arc3,rad={r}\" for r in it.accumulate([0.04] * 4)]\n", - "nx.draw_networkx_edges(G, pos, edge_color=\"black\", width=.4, connectionstyle=connectionstyle, min_source_margin=5, min_target_margin=5, style='dotted', arrowsize=5, node_size=1000)\n", - "nx.draw_networkx_labels(G, pos, font_size=4, font_family=\"sans-serif\", font_color=\"white\")\n", - "nx.draw_networkx_edge_labels(G,pos, font_size=3, connectionstyle=connectionstyle, edge_labels={(u, v): d['weight'] for u, v, d in G.edges(data=True)})\n", + "nx.draw_networkx_edges(\n", + " G,\n", + " pos,\n", + " edge_color=\"black\",\n", + " width=0.4,\n", + " connectionstyle=connectionstyle,\n", + " min_source_margin=5,\n", + " min_target_margin=5,\n", + " style=\"dotted\",\n", + " arrowsize=5,\n", + " node_size=1000,\n", + ")\n", + "nx.draw_networkx_labels(\n", + " G, pos, font_size=4, font_family=\"sans-serif\", font_color=\"white\"\n", + ")\n", + "nx.draw_networkx_edge_labels(\n", + " G,\n", + " pos,\n", + " font_size=3,\n", + " connectionstyle=connectionstyle,\n", + " edge_labels={(u, v): d[\"weight\"] for u, v, d in G.edges(data=True)},\n", + ")\n", "\n", "plt.axis(\"off\")\n", "plt.show()" @@ -271,57 +325,24 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "weth = g[Vertex(\"WETH\", 18, Web3.to_checksum_address(\"0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2\"))][0][0]\n", - "usdc = g[Vertex(\"WETH\", 18, Web3.to_checksum_address(\"0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2\"))][0][1]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[([WETH, USDC, (7.832425629907495, (WETH, USDC, '500'))]),\n", - " ([WETH, USDT, (7.833202206878353, (WETH, USDT, '500'))]),\n", - " ([WETH, USDC, (7.831326751334814, (WETH, USDC, '3000'))]),\n", - " ([WETH, WBTC, (-3.2723494311571666, (WETH, WBTC, '500'))])]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "g[weth]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[([USDC, WETH, (-7.832425629907495, (USDC, WETH, '500'))]),\n", - " ([USDC, USDT, (0.0009050532828726206, (USDC, USDT, '100'))]),\n", - " ([USDC, WETH, (-7.831326751334814, (USDC, WETH, '3000'))])]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "g[usdc]" + "weth = g[\n", + " Vertex(\n", + " \"WETH\",\n", + " 18,\n", + " Web3.to_checksum_address(\"0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2\"),\n", + " )\n", + "][0][0]\n", + "usdc = g[\n", + " Vertex(\n", + " \"WETH\",\n", + " 18,\n", + " Web3.to_checksum_address(\"0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2\"),\n", + " )\n", + "][0][1]" ] } ], diff --git a/pyproject.toml b/pyproject.toml index 3ef55b1..767debe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,10 +4,8 @@ build-backend = "setuptools.build_meta" [project] name = "defigraph" -version = "0.0.2" -authors = [ - { name="Meek Msaki", email="meek.dev3@gmail.com" }, -] +version = "0.1.1" +authors = [{ name = "Meek Msaki", email = "meek.dev3@gmail.com" }] description = "A package for creating a directed graphs based on DeFi protocols" readme = "README.md" requires-python = ">=3.8" @@ -16,18 +14,15 @@ classifiers = [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ] +dependencies = ["pytest>=8.3.3", "ruff>=0.7.4", "web3>=7.5.0"] [project.urls] Homepage = "https://github.com/mmsaki/defigraph" Issues = "https://github.com/mmsaki/defigraph/issues" [tool.pytest.ini_options] -pythonpath = [ - ".", "src", -] -addopts = [ - "--import-mode=importlib", -] +pythonpath = [".", "src"] +addopts = ["--import-mode=importlib"] [project.optional-dependencies] dev = ["bumpver", "pytest"] @@ -43,6 +38,4 @@ tag = true push = true [tool.bumpver.file_patterns] -"pyproject.toml" = [ - 'current_version = "{version}"', -] \ No newline at end of file +"pyproject.toml" = ['current_version = "{version}"'] diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index f247600..0000000 --- a/requirements.txt +++ /dev/null @@ -1,157 +0,0 @@ -accessible-pygments==0.0.5 -aiohappyeyeballs==2.4.3 -aiohttp==3.10.10 -aiosignal==1.3.1 -alabaster==0.7.16 -annotated-types==0.7.0 -appnope==0.1.4 -asttokens==2.4.1 -async-timeout==4.0.3 -attrs==24.2.0 -babel==2.16.0 -beautifulsoup4==4.12.3 -bitarray==3.0.0 -bleach==6.1.0 -build==1.2.2.post1 -bumpver==2023.1129 -certifi==2024.8.30 -charset-normalizer==3.4.0 -ckzg==2.0.1 -click==8.1.7 -colorama==0.4.6 -comm==0.2.2 -contourpy==1.3.0 -cycler==0.12.1 -cytoolz==1.0.0 -debugpy==1.8.7 -decorator==5.1.1 -defusedxml==0.7.1 -docutils==0.20.1 -eth-account==0.13.4 -eth-hash==0.7.0 -eth-keyfile==0.8.1 -eth-keys==0.5.1 -eth-rlp==2.1.0 -eth-typing==5.0.1 -eth-utils==5.0.0 -eth_abi==5.1.0 -exceptiongroup==1.2.2 -executing==2.1.0 -fastjsonschema==2.20.0 -fonttools==4.54.1 -frozenlist==1.4.1 -greenlet==3.1.1 -hexbytes==1.2.1 -idna==3.10 -imagesize==1.4.1 -importlib_metadata==8.5.0 -iniconfig==2.0.0 -ipykernel==6.29.5 -ipython==8.28.0 -jedi==0.19.1 -Jinja2==3.1.4 -jsonschema==4.23.0 -jsonschema-specifications==2024.10.1 -jupyter-cache==1.0.0 -jupyter_client==8.6.3 -jupyter_core==5.7.2 -jupyterlab_pygments==0.3.0 -kiwisolver==1.4.7 -latexcodec==3.0.0 -lexid==2021.1006 -linkify-it-py==2.0.3 -looseversion==1.3.0 -markdown-it-py==3.0.0 -MarkupSafe==3.0.2 -matplotlib==3.9.2 -matplotlib-inline==0.1.7 -mdit-py-plugins==0.4.2 -mdurl==0.1.2 -mistune==3.0.2 -multidict==6.1.0 -myst-nb==1.1.2 -myst-parser==2.0.0 -nbclient==0.10.0 -nbconvert==7.16.4 -nbformat==5.10.4 -nbsphinx==0.9.5 -nest-asyncio==1.6.0 -networkx==3.4.1 -numpy==2.1.2 -packaging==24.1 -pandoc==2.4 -pandocfilters==1.5.1 -parsimonious==0.10.0 -parso==0.8.4 -pexpect==4.9.0 -pillow==11.0.0 -platformdirs==4.3.6 -pluggy==1.5.0 -plumbum==1.9.0 -ply==3.11 -prompt_toolkit==3.0.48 -propcache==0.2.0 -psutil==6.1.0 -ptyprocess==0.7.0 -pure_eval==0.2.3 -pybtex==0.24.0 -pybtex-docutils==1.0.3 -pycryptodome==3.21.0 -pydantic==2.9.2 -pydantic_core==2.23.4 -pydata-sphinx-theme==0.15.4 -Pygments==2.18.0 -pyparsing==3.2.0 -pyproject_hooks==1.2.0 -pytest==8.3.3 -python-dateutil==2.9.0.post0 -python-dotenv==1.0.1 -pyunormalize==16.0.0 -PyYAML==6.0.2 -pyzmq==26.2.0 -referencing==0.35.1 -regex==2024.9.11 -requests==2.32.3 -rlp==4.0.1 -rpds-py==0.20.0 -six==1.16.0 -snowballstemmer==2.2.0 -soupsieve==2.6 -Sphinx==7.4.7 -sphinx-book-theme==1.1.3 -sphinx-comments==0.0.3 -sphinx-copybutton==0.5.2 -sphinx-jupyterbook-latex==1.0.0 -sphinx-multitoc-numbering==0.1.3 -sphinx-rtd-theme==3.0.1 -sphinx-thebe==0.3.1 -sphinx-togglebutton==0.3.2 -sphinx_design==0.6.1 -sphinx_external_toc==1.0.1 -sphinxcontrib-applehelp==2.0.0 -sphinxcontrib-bibtex==2.6.3 -sphinxcontrib-devhelp==2.0.0 -sphinxcontrib-htmlhelp==2.1.0 -sphinxcontrib-jquery==4.1 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==2.0.0 -sphinxcontrib-serializinghtml==2.0.0 -SQLAlchemy==2.0.36 -stack-data==0.6.3 -tabulate==0.9.0 -tinycss2==1.3.0 -toml==0.10.2 -tomli==2.0.2 -toolz==1.0.0 -tornado==6.4.1 -traitlets==5.14.3 -types-requests==2.32.0.20241016 -typing_extensions==4.12.2 -uc-micro-py==1.0.3 -urllib3==2.2.3 -wcwidth==0.2.13 -web3==7.4.0 -webencodings==0.5.1 -websockets==13.1 -yarl==1.15.5 -zipp==3.20.2 diff --git a/src/defigraph/Edge.py b/src/defigraph/Edge.py index cb53ac7..c4d82f4 100644 --- a/src/defigraph/Edge.py +++ b/src/defigraph/Edge.py @@ -1,24 +1,24 @@ from defigraph.Pool import Pool from math import log + class Edge: - def __init__(self, pool: Pool): - self.pool = pool - self.weight = -log(self.pool.token0_price) - self._tuple = [self.pool.token0, self.pool.token1, (self.weight, self.pool)] + def __init__(self, pool: Pool): + self.pool = pool + self.weight = -log(self.pool.token0_price) + self._tuple = [self.pool.token0, self.pool.token1, (self.weight, self.pool)] + + def __repr__(self): + return f"({[i for i in self._tuple]})" + + def __eq__(self, edge): + return self.pool == edge.pool + + def __ne__(self, edge): + return self.pool != edge.pool - def __repr__(self): - return f"({[i for i in self._tuple]})" - - def __eq__(self, edge): - return self.pool == edge.pool - - def __ne__(self, edge): - return self.pool != edge.pool + def __iter__(self): + return (i for i in self._tuple) - def __iter__(self): - return (i for i in self._tuple) - - def __getitem__(self, index): - return self._tuple[index] - \ No newline at end of file + def __getitem__(self, index): + return self._tuple[index] diff --git a/src/defigraph/Graph.py b/src/defigraph/Graph.py index 3205719..21bd319 100644 --- a/src/defigraph/Graph.py +++ b/src/defigraph/Graph.py @@ -3,87 +3,92 @@ from defigraph.Edge import Edge from defigraph.Pool import Pool + class Graph: - def __init__(self, edges: List[Edge]) -> None: - self.vertices: List[Vertex] = [] - self.adjascency_list: Dict[Vertex, List[Edge]] = {} + def __init__(self, edges: List[Edge]) -> None: + self.vertices: List[Vertex] = [] + self.adjascency_list: Dict[Vertex, List[Edge]] = {} + + for edge in edges: + # Create adjacency list + if edge.pool.token0 not in self.adjascency_list: + self.adjascency_list[edge.pool.token0] = [edge] + else: + if edge not in self.adjascency_list[edge.pool.token0]: + self.adjascency_list[edge.pool.token0].append(edge) + # Create vertices list + if edge.pool.token0 not in self.vertices: + self.vertices.append(edge.pool.token0) + if edge.pool.token1 not in self.vertices: + self.vertices.append(edge.pool.token1) + + def __repr__(self) -> str: + return f"{self.adjascency_list}" + + def __getitem__(self, vertex: str) -> List[Edge]: + return self.adjascency_list[vertex] + + def __setitem__(self, vertex: str, edges: List[Vertex]) -> None: + if vertex not in self.adjascency_list: + self.adjascency_list[vertex] = edges + else: + for edge in edges: + if edge not in self.adjascency_list[vertex]: + self.adjascency_list[vertex].append(edge) + + def __len__(self): + return len(self.vertices) - for edge in edges: - # Create adjacency list - if edge.pool.token0 not in self.adjascency_list: - self.adjascency_list[edge.pool.token0] = [edge] - else: - if edge not in self.adjascency_list[edge.pool.token0]: - self.adjascency_list[edge.pool.token0].append(edge) - # Create vertices list - if edge.pool.token0 not in self.vertices: - self.vertices.append(edge.pool.token0) - if edge.pool.token1 not in self.vertices: - self.vertices.append(edge.pool.token1) + def add_edge(self, pool: Pool, directed=False) -> None: + edge = Edge(pool=pool) - def __repr__(self) -> str: - return f"{self.adjascency_list}" + if edge.pool.token0 not in self.adjascency_list: + self.adjascency_list[edge.pool.token0] = [edge] + else: + if edge not in self.adjascency_list[edge.pool.token0]: + self.adjascency_list[edge.pool.token0].append(edge) - def __getitem__(self, vertex: str) -> List[Edge]: - return self.adjascency_list[vertex] - - def __setitem__(self, vertex: str, edges: List[Vertex]) -> None: - if vertex not in self.adjascency_list: - self.adjascency_list[vertex] = edges - else: - for edge in edges: - if edge not in self.adjascency_list[vertex]: - self.adjascency_list[vertex].append(edge) - - def __len__(self): - return len(self.vertices) + # add the undirected edge + if not directed: + token0 = edge.pool.token0 + token1 = edge.pool.token1 + pool_address = edge.pool.address + fee = edge.pool.fee + token0price = edge.pool.token0_price + token1price = edge.pool.token1_price + reverse_pool_direction = Pool( + token0=token1, + token1=token0, + pool_address=pool_address, + fee=fee, + token0_price=token1price, + token1_price=token0price, + ) + edge = Edge(pool=reverse_pool_direction) - def add_edge(self, pool: Pool, directed=False) -> None: - """Directed=False means you can trade in any direction between token0 and token1""" - edge = Edge(pool=pool) + if edge.pool.token0 not in self.adjascency_list: + self.adjascency_list[edge.pool.token0] = [edge] + else: + if edge not in self.adjascency_list[edge.pool.token0]: + self.adjascency_list[edge.pool.token0].append(edge) - if edge.pool.token0 not in self.adjascency_list: - self.adjascency_list[edge.pool.token0] = [edge] - else: - if edge not in self.adjascency_list[edge.pool.token0]: - self.adjascency_list[edge.pool.token0].append(edge) + # Update vertices list + if edge.pool.token0 not in self.vertices: + self.vertices.append(edge.pool.token0) + if edge.pool.token1 not in self.vertices: + self.vertices.append(edge.pool.token1) - # add the undirected edge - if not directed: - token0 = edge.pool.token0 - token1 = edge.pool.token1 - pool_address = edge.pool.address - fee = edge.pool.fee - token0price = edge.pool.token0_price - token1price = edge.pool.token1_price - reverse_pool_direction = Pool(token0=token1,token1=token0, pool_address=pool_address, fee=fee, token0_price=token1price, token1_price=token0price) - edge = Edge(pool=reverse_pool_direction) + def get_edge_count(self) -> int: + count = 0 + for vertex in self.adjascency_list: + count += len(self.adjascency_list[vertex]) + return count - if edge.pool.token0 not in self.adjascency_list: - self.adjascency_list[edge.pool.token0] = [edge] - else: - if edge not in self.adjascency_list[edge.pool.token0]: - self.adjascency_list[edge.pool.token0].append(edge) + def get_vertices(self) -> List[Vertex]: + return self.vertices - # Update vertices list - if edge.pool.token0 not in self.vertices: - self.vertices.append(edge.pool.token0) - if edge.pool.token1 not in self.vertices: - self.vertices.append(edge.pool.token1) - - def get_edge_count(self) -> int: - count = 0 - for vertex in self.adjascency_list: - count += len(self.adjascency_list[vertex]) - return count - - def get_vertices(self) -> List[Vertex]: - return self.vertices - - def get_edges(self) -> List[Edge]: - edges = [] - for vertex in self.adjascency_list: - edges.extend(self.adjascency_list[vertex]) - return edges - - \ No newline at end of file + def get_edges(self) -> List[Edge]: + edges = [] + for vertex in self.adjascency_list: + edges.extend(self.adjascency_list[vertex]) + return edges diff --git a/src/defigraph/Pool.py b/src/defigraph/Pool.py index 2502019..15c7443 100644 --- a/src/defigraph/Pool.py +++ b/src/defigraph/Pool.py @@ -1,26 +1,42 @@ from defigraph.Vertex import Vertex from web3 import Web3 + class Pool: - def __init__(self, pool_address: str, token0: Vertex, token1: Vertex, fee: int, token0_price: float, token1_price: float): - if token1 == token0: raise ValueError("Token0 should not equal Token1") - if not Web3.is_checksum_address(pool_address): raise ValueError("Address is not a valid checksum address") - if type(token0_price) != float and type(token1_price) != float: raise ValueError(f"Token prices should be of type {float}") - self.address = pool_address - self.token0 = token0 - self.token1 = token1 - self.token0_price = token0_price - self.token1_price = token1_price - self.fee = fee - - def __repr__(self): - return f"{(self.token0, self.token1, self.fee)}" - - def __eq__(self, pool): - return self.address == pool.address - - def __ne__(self, pool): - return self.address != pool.address - - def __hash__(self): - return hash(str(self)) + def __init__( + self, + pool_address: str, + token0: Vertex, + token1: Vertex, + fee: int, + token0_price: float, + token1_price: float, + ): + if token1 == token0: + raise ValueError("Token0 should not equal Token1") + if not Web3.is_checksum_address(pool_address): + raise ValueError("Address is not a valid checksum address") + + type_toke0 = isinstance(token0_price, float) + type_toke1 = isinstance(token1_price, float) + + if not type_toke0 and not type_toke1: + raise ValueError(f"Token prices should be of type {float}") + self.address = pool_address + self.token0 = token0 + self.token1 = token1 + self.token0_price = token0_price + self.token1_price = token1_price + self.fee = fee + + def __repr__(self): + return f"{(self.token0, self.token1, self.fee)}" + + def __eq__(self, pool): + return self.address == pool.address + + def __ne__(self, pool): + return self.address != pool.address + + def __hash__(self): + return hash(str(self)) diff --git a/src/defigraph/Vertex.py b/src/defigraph/Vertex.py index d684d37..3d5c35a 100644 --- a/src/defigraph/Vertex.py +++ b/src/defigraph/Vertex.py @@ -1,22 +1,35 @@ from web3 import Web3 + class Vertex: - def __init__(self, name: str, decimals: int, address: str): - if type(decimals) != int: raise TypeError(f"Decimals should be type {int} but receved {type(decimals)}") - if not Web3.is_checksum_address(address): raise ValueError(f"Expected checksum address but received {address}") - - self.name = name - self.decimals = decimals - self.address = address + def __init__(self, name: str, decimals: int, address: str): + if not isinstance(decimals, int): + raise TypeError( + f"Decimals should be type {int} but receved {type(decimals)}" + ) + if not Web3.is_checksum_address(address): + raise ValueError(f"Expected checksum address but received {address}") + + self.name = name + self.decimals = decimals + self.address = address - def __repr__(self): - return f"{self.name}" + def __repr__(self): + return f"{self.name}" - def __eq__(self, vertex): - return self.name == vertex.name and self.decimals == vertex.decimals and self.address == vertex.address + def __eq__(self, vertex): + return ( + self.name == vertex.name + and self.decimals == vertex.decimals + and self.address == vertex.address + ) - def __ne__(self, vertex): - return self.name != vertex.name or self.decimals != vertex.decimals or eval(self.address) != eval(vertex.address) + def __ne__(self, vertex): + return ( + self.name != vertex.name + or self.decimals != vertex.decimals + or eval(self.address) != eval(vertex.address) + ) - def __hash__(self): - return hash(str(self)) \ No newline at end of file + def __hash__(self): + return hash(str(self)) diff --git a/tests/Edge_test.py b/tests/Edge_test.py index 19645b9..0352069 100644 --- a/tests/Edge_test.py +++ b/tests/Edge_test.py @@ -1,7 +1,9 @@ import unittest + class TestEdgeMethods(unittest.TestCase): - pass + pass + if __name__ == "__main__": - unittest.main() \ No newline at end of file + unittest.main() diff --git a/tests/Graph_test.py b/tests/Graph_test.py index b6d78cf..48eb61a 100644 --- a/tests/Graph_test.py +++ b/tests/Graph_test.py @@ -1,7 +1,9 @@ import unittest + class TestGraphMethods(unittest.TestCase): - pass + pass + if __name__ == "__main__": - unittest.main() \ No newline at end of file + unittest.main() diff --git a/tests/Pool_test.py b/tests/Pool_test.py index 6668189..101a691 100644 --- a/tests/Pool_test.py +++ b/tests/Pool_test.py @@ -4,51 +4,75 @@ import pytest from web3 import Web3 + class TestPoolMethods(unittest.TestCase): - pool = { - "feeTier": "500", - "id": "0x88e6a0c2ddd26feeb64f039a2c41296fcb3f5640", - "token0": { - "decimals": "6", - "id": "0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48", - "name": "USD Coin", - "symbol": "USDC" - }, - "token0Price": "2582.932548000005213781395752324381", - "token1": { - "decimals": "18", - "id": "0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2", - "name": "Wrapped Ether", - "symbol": "WETH" - }, - "token1Price": "0.0003871568387545821353167124452051942" - } - fee = pool['feeTier'] - token0 = pool['token0']['symbol'] - token0_decimals = int(pool['token0']['decimals']) - token0_address = Web3.to_checksum_address(pool['token0']['id']) - token0Price = float(pool['token0Price']) - - token1 = pool['token1']['symbol'] - token1_decimals = int(pool['token1']['decimals']) - token1_address = Web3.to_checksum_address(pool['token1']['id']) - token1Price = float(pool['token1Price']) - - pool_address = Web3.to_checksum_address(pool['id']) - - u = Vertex(token0, token0_decimals, token0_address) - v = Vertex(token1, token1_decimals, token1_address) - - def test_token0_not_equal_to_token1(self): - with pytest.raises(ValueError, match="Token0 should not equal Token1"): - Pool(pool_address=self.pool_address, token0=self.u, token1=self.u, fee=self.fee, token0_price=self.token0Price, token1_price=self.token1Price) - - def test_pool_address_should_be_valid_checksum(self): - with pytest.raises(ValueError, match="Address is not a valud checksum address"): - Pool(pool_address=self.pool['id'], token0=self.u, token1=self.v, fee=self.fee, token0_price=self.token0Price, token1_price=self.token1Price) - - def test_token_prices_should_be_type_float(self): - with pytest.raises(ValueError, match=f"Token prices should be of type {float}"): - Pool(pool_address=self.pool_address, token0=self.u, token1=self.v, fee=self.fee, token0_price=str(self.token0Price), token1_price=str(self.token1Price)) + pool = { + "feeTier": "500", + "id": "0x88e6a0c2ddd26feeb64f039a2c41296fcb3f5640", + "token0": { + "decimals": "6", + "id": "0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48", + "name": "USD Coin", + "symbol": "USDC", + }, + "token0Price": "2582.932548000005213781395752324381", + "token1": { + "decimals": "18", + "id": "0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2", + "name": "Wrapped Ether", + "symbol": "WETH", + }, + "token1Price": "0.0003871568387545821353167124452051942", + } + fee = pool["feeTier"] + token0 = pool["token0"]["symbol"] + token0_decimals = int(pool["token0"]["decimals"]) + token0_address = Web3.to_checksum_address(pool["token0"]["id"]) + token0Price = float(pool["token0Price"]) + + token1 = pool["token1"]["symbol"] + token1_decimals = int(pool["token1"]["decimals"]) + token1_address = Web3.to_checksum_address(pool["token1"]["id"]) + token1Price = float(pool["token1Price"]) + + pool_address = Web3.to_checksum_address(pool["id"]) + + u = Vertex(token0, token0_decimals, token0_address) + v = Vertex(token1, token1_decimals, token1_address) + + def test_token0_not_equal_to_token1(self): + with pytest.raises(ValueError, match="Token0 should not equal Token1"): + Pool( + pool_address=self.pool_address, + token0=self.u, + token1=self.u, + fee=self.fee, + token0_price=self.token0Price, + token1_price=self.token1Price, + ) + + def test_pool_address_should_be_valid_checksum(self): + with pytest.raises(ValueError, match="Address is not a valid checksum address"): + Pool( + pool_address=self.pool["id"], + token0=self.u, + token1=self.v, + fee=self.fee, + token0_price=self.token0Price, + token1_price=self.token1Price, + ) + + def test_token_prices_should_be_type_float(self): + with pytest.raises(ValueError, match=f"Token prices should be of type {float}"): + Pool( + pool_address=self.pool_address, + token0=self.u, + token1=self.v, + fee=self.fee, + token0_price=str(self.token0Price), + token1_price=str(self.token1Price), + ) + + if __name__ == "__main__": - unittest.main() \ No newline at end of file + unittest.main() diff --git a/tests/Vertex_test.py b/tests/Vertex_test.py index a7f95c0..cb8f447 100644 --- a/tests/Vertex_test.py +++ b/tests/Vertex_test.py @@ -3,44 +3,45 @@ import pytest from web3 import Web3 + class TestVertexMethods(unittest.TestCase): - def setUp(self): - self.pool = { - "feeTier": "500", - "id": "0x88e6a0c2ddd26feeb64f039a2c41296fcb3f5640", - "token0": { - "decimals": "6", - "id": "0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48", - "name": "USD Coin", - "symbol": "USDC" - }, - "token0Price": "2582.932548000005213781395752324381", - "token1": { - "decimals": "18", - "id": "0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2", - "name": "Wrapped Ether", - "symbol": "WETH" - }, - "token1Price": "0.0003871568387545821353167124452051942" - } + def setUp(self): + self.pool = { + "feeTier": "500", + "id": "0x88e6a0c2ddd26feeb64f039a2c41296fcb3f5640", + "token0": { + "decimals": "6", + "id": "0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48", + "name": "USD Coin", + "symbol": "USDC", + }, + "token0Price": "2582.932548000005213781395752324381", + "token1": { + "decimals": "18", + "id": "0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2", + "name": "Wrapped Ether", + "symbol": "WETH", + }, + "token1Price": "0.0003871568387545821353167124452051942", + } + + def test_address_should_be_valid_checksum(self): + with pytest.raises(ValueError): + name = self.pool["token0"] + decimals = int(self.pool["token0"]["decimals"]) + address = self.pool["token0"]["id"] + Vertex(name, decimals, address) + + def test_fails_if_decimals_is_string(self): + with pytest.raises(TypeError): + name = self.pool["token0"] + decimals = self.pool["token0"]["decimals"] + address = Web3.to_checksum_address(self.pool["token0"]["id"]) + Vertex(name, decimals, address) - def test_address_should_be_valid_checksum(self): - with pytest.raises(ValueError): - name = self.pool['token0'] - decimals = 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