""" Post trades from Gemini to MongoDB """

from gemini.client import Client
from pymongo import MongoClient

import os
import datetime
import time

def calc_sma(prices, periods):
    # TODO: implement later
    if len(prices) < periods:
        return 0
    else:
        return int(sum(prices[-periods::])/len(prices[-periods::]))

def calc_ema(prices, periods, ema):
    # TODO: implement later
    multiplier = (2 / (periods + 1))

    if len(ema) == 0:
        return 0
    else:
        current_price = prices[-1]
        prev_ema = ema[-1]
        return ((current_price - prev_ema)*multiplier)+prev_ema

if __name__ == '__main__':
    # connect to Mongo
    try:
        client = MongoClient('mongodb://localhost:27017')
    except:
        print("Cannot connect to Mongo")

    # epoch timestamps with ms
    current_time = int(time.time()*1000)

    # client object for the exchange
    c = Client(os.environ['GEMINI_API_KEY'], os.environ['GEMINI_API_SECRET'])

    # select database
    db = client['trades']
    gemini = db['gemini']

    # figure out where we are starting
    pipeline = [
        {
            "$group": {
                "_id": "Latest Record",
                "latest": {
                    "$max": "$timestampms"
                }
            }
        }
    ]

    latest = list(db.gemini.aggregate(pipeline))
    try:
        start_time = latest[0]['latest']+1
    except IndexError:
        # new collection - start over with 1/1/2015
        start_time = 1420070400000
        print("Blank collection...setting new time")

    while start_time < current_time:

        s = start_time / 1000.0
        print(datetime.datetime.fromtimestamp(s).strftime('%Y-%m-%d %H:%M:%S.%f'))

        # list to hold documents for bulk inserts
        doc = list()

        data = c.get_trade_history(symbol='btcusd', since=start_time, limit_trades=500)
        if data:
            data.reverse()

            for d in data:
                if d['timestampms'] > start_time:
                    start_time = d['timestampms']+1

                doc.append({
                        "price": str(d['price']),
                        "timestampms": d['timestampms'],
                        "tid": d['tid'],
                        "type": str(d['type']),
                        "timestamp": d['timestamp'],
                        "amount": d['amount'],
                        "exchange": str(d['exchange'])
                    })

            # bulk insert this batch
            doc_ids = gemini.insert_many(doc)
        else:
            "Incrementing start time"
            start_time += 1

    # cleanup: remove any duplicate transaction id's
    pipeline = [{
        "$group": {
            "_id": {
                "tid": "$tid"
            },
            "count": {
                "$sum": 1
            }
        }
    },{
        "$match": {
            "count": {
                "$gt": 1
            }
        }
    }]

    dups = list(db.gemini.aggregate(pipeline))
    for d in dups:
        deleteOne = gemini.delete_one(d['_id'])
        print("Cleaning up duplicate tid's...")

    print("Done\n")

    # print some stats/sanity checks
    stats = db.command("collstats", "gemini")
    dups = list(db.gemini.aggregate(pipeline))

    print("Size on disk:\t%s" % stats['storageSize'])
    print("Number of documents:\t%s" % gemini.count())
    print("Duplicate tid's:\t%s" % len(dups))