add bid ask price
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@ -9,6 +9,7 @@ app/*.sh
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app/.env*
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app/ml_models/weights
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app/ml_models/training_data
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app/ml_models/__pycache__
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app/json/*
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app/logs/*
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fastify/node_modules
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@ -43,6 +43,16 @@ async def get_pre_post_quote_of_stocks(ticker_list):
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else:
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return {}
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async def get_bid_ask_quote_of_stocks(ticker_list):
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ticker_str = ','.join(ticker_list)
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async with aiohttp.ClientSession() as session:
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url = f"https://financialmodelingprep.com/api/v4/batch-pre-post-market/{ticker_str}?apikey={api_key}"
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async with session.get(url) as response:
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if response.status == 200:
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return await response.json()
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else:
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return {}
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async def save_quote_as_json(symbol, data):
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with open(f"json/quote/{symbol}.json", 'w') as file:
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ujson.dump(data, file)
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@ -58,6 +68,25 @@ async def save_pre_post_quote_as_json(symbol, data):
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except Exception as e:
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pass
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async def save_bid_ask_as_json(symbol, data):
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try:
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# Read previous close price and load existing quote data
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with open(f"json/quote/{symbol}.json", 'r') as file:
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quote_data = ujson.load(file)
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# Update quote data with new price, ask, bid, changesPercentage, and timestamp
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quote_data.update({
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'ask': round(data['ask'], 2), # Add ask price
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'bid': round(data['bid'], 2), # Add bid price
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})
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# Save the updated quote data back to the same JSON file
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with open(f"json/quote/{symbol}.json", 'w') as file:
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ujson.dump(quote_data, file)
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except Exception as e:
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print(f"An error occurred: {e}") # Print the error for debugging
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async def run():
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con = sqlite3.connect('stocks.db')
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etf_con = sqlite3.connect('etf.db')
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@ -65,7 +94,7 @@ async def run():
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cursor = con.cursor()
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cursor.execute("PRAGMA journal_mode = wal")
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol != ?", ('%5EGSPC',))
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cursor.execute("SELECT DISTINCT symbol FROM stocks")
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stocks_symbols = [row[0] for row in cursor.fetchall()]
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etf_cursor = etf_con.cursor()
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@ -100,7 +129,7 @@ async def run():
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total_symbols = stocks_symbols+etf_symbols
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chunk_size = len(total_symbols) // 10 # Divide the list into 10 chunks
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chunk_size = len(total_symbols) // 20 # Divide the list into N chunks
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chunks = [total_symbols[i:i + chunk_size] for i in range(0, len(total_symbols), chunk_size)]
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delete_files_in_directory("json/pre-post-quote")
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for chunk in chunks:
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@ -117,7 +146,11 @@ async def run():
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symbol = item['symbol']
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await save_pre_post_quote_as_json(symbol, item)
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#print(f"Saved data for {symbol}.")
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#Always true
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bid_ask_quote = await get_bid_ask_quote_of_stocks(chunk)
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for item in bid_ask_quote:
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symbol = item['symbol']
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await save_bid_ask_as_json(symbol, item)
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try:
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asyncio.run(run())
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@ -61,7 +61,6 @@ const corsMiddleware = (request, reply, done) => {
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fastify.addHook("onRequest", corsMiddleware);
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//fastify.register(require('./mixpanel/server'), { mixpanel, UAParser });
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fastify.register(require("./get-all-comments/server"), { pb });
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fastify.register(require("./get-post/server"), { pb });
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fastify.register(require("./get-one-post/server"), { pb });
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fastify.register(require("./get-portfolio-data/server"), { pb });
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@ -1,55 +0,0 @@
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// Declare a route
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function listToTree(comments, parentProp = "reply") {
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// Create id indexed comments dictionary
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const commentsDict = {};
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for (let comment of comments) {
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commentsDict[comment.id] = {
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...comment,
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children: [],
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};
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}
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// Build the tree
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const tree = [];
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for (const comment of comments) {
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const parentId = comment[parentProp];
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if (parentId) {
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commentsDict[parentId].children.push(commentsDict[comment.id]);
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} else {
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tree.push(commentsDict[comment.id]);
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}
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}
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return tree;
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}
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module.exports = function (fastify, opts, done) {
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const pb = opts.pb;
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fastify.post('/get-all-comments', async (request, reply) => {
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const data = request.body;
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const postId = data?.postId
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let output;
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try {
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const result = await pb.collection("comments").getFullList({
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filter: `post="${postId}"`,
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expand: 'user,alreadyVoted(comment)',
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fields: "*,expand.user,expand.alreadyVoted(comment).user,expand.alreadyVoted(comment).type",
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sort: '-created',
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})
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output = listToTree(result);
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}
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catch(e) {
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output = [];
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}
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+ reply.send({ items: output })
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});
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done();
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};
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