def fetch_tech_indicators():
with open("./store/indicators.json", 'w') as indicators_json:
try:
price_data = dataset("price_data")
indicators = transformer("calculate_indicators")(price_data)
get_signal = {
"MOM (1)": lambda v: "BUY" if v >= 0 else "SELL",
"ADX (14)": lambda v: "BUY"
if v >= 25 else "SELL", # Not sure about this
"WILLR": lambda v: "SELL" if v <= -50 else "BUY",
"RSI (6)": lambda v: "SELL" if v >= 50 else "BUY",
"ATR (14)": lambda v: "N/A",
"OBV": lambda v: "N/A",
"TRIX (20)": lambda v: "N/A",
"EMA (6)": lambda v: "N/A"
}
data = []
for indicator, signal in get_signal.items():
val = round(indicators[indicator][0], 2)
data.append([indicator, str(val), signal(val)])
indicators_json.write(
json.dumps(
{
"error": False,
"data": list(sorted(data, key=lambda i: len(i[0])))
},
indent=2))
except:
indicators_json.write(
json.dumps({
"error": True,
"data": []
}, indent=2))
def fetch_tech_indicators():
# TODO: Create a mapping between indicator values and signals
with open("./cache/data/indicators.json", 'w') as indicators_json:
try:
indicators = transformer("calculate_indicators")(
dataset("price_data"))
# "MACD": {}, # MACD, MACD (Signal), MACD (Historical)
# "MOM (1)": {"value": indicators["MOM (1)"][0], "signal": ""},
# "ADX (20)": {"value": indicators["ADX (20)"][0], "signal": ""},
# "RSI (12)": {"value": indicators["RSI (12)"][0], "signal": ""},
# "ATR (14)": {"value": indicators["ATR (14)"][0], "signal": ""},
# "OBV": {"value": indicators["OBV"][0], "signal": ""},
# "TRIX (20)": {"value": indicators["TRIX (20)"][0], "signal": ""},
# "EMA (6)": {"value": indicators["EMA (6)"][0], "signal": "NONE"},
data = [[
"MOM (3-period)",
str(round(indicators["MOM (1)"][0], 2)), "SELL"
],
[
"ADX (14-period)",
str(round(indicators["ADX (14)"][0], 2)), "SELL"
],
["WILLR",
str(round(indicators["WILLR"][0], 2)), "SELL"],
[
"RSI (6-period)",
str(round(indicators["RSI (6)"][0], 2)), "SELL"
],
[
"ATR (14-period)",
str(round(indicators["ATR (14)"][0], 2)), "SELL"
], ["OBV",
str(round(indicators["OBV"][0], 2)), "BUY"],
[
"TRIX (20-period)",
str(round(indicators["TRIX (20)"][0], 2)), "BUY"
],
[
"EMA (6-period)",
str(round(indicators["EMA (6)"][0], 2)), "BUY"
]]
indicators_json.write(
json.dumps(
{
"error": False,
"data": list(sorted(data, key=lambda i: len(i[0])))
},
indent=2))
except:
indicators_json.write(
json.dumps({
"error": True,
"data": []
}, indent=2))
def make_prediction():
price_data = dataset("price_data")
blockchain_data = dataset("blockchain_data")
processed_data = (price_data.pipe(
transformer("calculate_indicators")).pipe(
transformer("merge_datasets"), other_sets=[blockchain_data]).pipe(
transformer("fix_null_vals")).pipe(
transformer("add_lag_vars")).pipe(
transformer("power_transform")).pipe(
transformer("binarize_labels")).drop("Date",
axis=1))
feature_vector = processed_data.drop("Trend", axis=1).iloc[0]
model = Model(processed_data.drop(processed_data.index[0]), hyperopt=False)
return model.predict(feature_vector.values)[0]
