def get_top_symbols(tsym='BTC', limit=20):
if limit:
coins = pd.DataFrame(top.get_top_coins(tsym='BTC', limit=limit))
else:
coins = pd.DataFrame(top.get_top_coins(tsym='BTC'))
symbols = list(coins['SYMBOL'].values[1:])
return symbols
def get_top_coin_names(tsym='BTC', limit=20):
if limit:
coins = pd.DataFrame(top.get_top_coins(tsym='BTC', limit=limit))
else:
coins = pd.DataFrame(top.get_top_coins(tsym='BTC'))
symbols = [coin.split()[0] for coin in list(coins['FULLNAME'].values[1:])]
return symbols
def store_top_n(n):
coins = top.get_top_coins('USD', limit = n)
#Iterate through list of coins
count = 0
for i in coins:
count += 1
print(count)
#Currently we're normalizing the data before we store it
download_dir = "normalized_2k_min_data/06-06/" + i["SYMBOL"] + ".csv"
#Open directory
csv = open(download_dir, "w")
#Write the header
columnTitleRow = "time, price\n"
csv.write(columnTitleRow)
#Get the data to add
data = get_past_day_price(i["SYMBOL"])
price = normalize(data["price"]) #Normalize Prices
time = data["time"]
#Loop through time and price data
for j in range(len(price)):
row = time[j] + "," + str(price[j]) + "\n"
csv.write(row)
return 0
def list_top_n(n):
coins = top.get_top_coins('USD', limit = n)
download_dir = "coin_list_" + str(n) + ".csv"
csv = open(download_dir, "w")
#csv.write("symbol" + "\n")
for i in coins:
print(i["SYMBOL"])
csv.write(i["SYMBOL"])
csv.write("\n")
return 0
def test_get_top_coins():
topcoin = top.get_top_coins('USD')
assert len(topcoin) == 21
btc = topcoin[1]
assert btc['SYMBOL'] == 'BTC'
assert btc['SUPPLY'] == 17296550
assert btc['FULLNAME'] == 'Bitcoin (BTC)'
assert btc['NAME'] == 'Bitcoin'
assert btc['ID'] == '1182'
assert btc['VOLUME24HOURTO'] == 178522647.1706209
def top_n(n):
coins = top.get_top_coins('USD', limit = n)
price_matrix = np.zeros((1441,1))
count = 0
for i in coins:
count += 1
#print(price_matrix.shape)
price_matrix = np.c_[price_matrix, get_past_day_price(i["SYMBOL"])]
#print(price_matrix)
print(count)
return price_matrix