df_markets["volume"] = df_markets["volume"].astype(float).round(
0).astype(int)
df_markets.sort_values(by=["market"], ascending=True, inplace=True)
df_markets.set_index("market", inplace=True)
print("Processing, please wait...")
ROW = 1
for market, data in df_markets.T.iteritems():
print(
f"[{ROW}/{len(df_markets)}] {market} {round((ROW/len(df_markets))*100, 2)}%"
)
try:
if int(data["volume"]) > 0:
ta = TechnicalAnalysis(
api.getHistoricalData(market, GRANULARITY, None))
ta.addEMA(12)
ta.addEMA(26)
ta.addATR(72)
df_1h = ta.getDataFrame()
df_1h["ema12ltema26"] = df_1h.ema12 < df_1h.ema26
df_1h_last = df_1h.tail(1)
# volatility over the last 72 hours
df_markets.at[market,
"atr72"] = float(df_1h_last[["atr72"
]].values[0][0])
df_markets["atr72_pcnt"] = (df_markets["atr72"] /
df_markets["price"] *
100).round(2)
df_markets.at[market, "buy_next"] = df_1h_last[
def technical_analysis(exchange: str, market: str, g1, g2, g3) -> str:
if exchange == 'binance':
if not isBinanceMarketValid(market):
return f"""
{header()}
<h4>Invalid Market!</h4>
<div class="d-grid gap-2 d-md-flex justify-content-md-end">
<a class="text-dark" href='/{exchange}'><button class="btn btn-primary me-md-2" type="button">Go Back</button></a>
</div>
{footer()}
"""
elif exchange == 'coinbasepro':
if not isCoinbaseMarketValid(market):
return f"""
{header()}
<h4>Invalid Market!</h4>
<div class="d-grid gap-2 d-md-flex justify-content-md-end">
<a class="text-dark" href='/{exchange}'><button class="btn btn-primary me-md-2" type="button">Go Back</button></a>
</div>
{footer()}
"""
else:
return "Invalid Exchange!"
if exchange == 'binance':
api = BPublicAPI()
if exchange == 'coinbasepro':
api = CPublicAPI()
ticker = api.getTicker(market)
ta = TechnicalAnalysis(api.getHistoricalData(market, g1, None))
ta.addAll()
df_15m = ta.getDataFrame()
df_15m_last = df_15m.tail(1)
ta = TechnicalAnalysis(api.getHistoricalData(market, g2, None))
ta.addAll()
df_1h = ta.getDataFrame()
df_1h_last = df_1h.tail(1)
ta = TechnicalAnalysis(api.getHistoricalData(market, g3, None))
ta.addAll()
df_6h = ta.getDataFrame()
df_6h_last = df_6h.tail(1)
if exchange == 'binance':
exchange_name = 'Binance'
elif exchange == 'coinbasepro':
exchange_name = 'Coinbase Pro'
rsi14_15m_class = 'table-normal'
rsi14_15m_desc = 'Uneventful'
if df_15m_last['rsi14'].values[0] > 70:
rsi14_15m_class = 'table-danger'
rsi14_15m_desc = 'Overbought (Sell)'
elif df_15m_last['rsi14'].values[0] < 30:
rsi14_15m_class = 'table-success'
rsi14_15m_desc = 'Oversold (Buy)'
rsi14_1h_class = 'table-normal'
rsi14_1h_desc = 'Uneventful'
if df_1h_last['rsi14'].values[0] > 70:
rsi14_1h_class = 'table-danger'
rsi14_1h_desc = 'Overbought (Sell)'
elif df_1h_last['rsi14'].values[0] < 30:
rsi14_1h_class = 'table-success'
rsi14_1h_desc = 'Oversold (Buy)'
rsi14_6h_class = 'table-normal'
rsi14_6h_desc = 'Uneventful'
if df_6h_last['rsi14'].values[0] > 70:
rsi14_6h_class = 'table-danger'
rsi14_6h_desc = 'Overbought (Sell)'
elif df_6h_last['rsi14'].values[0] < 30:
rsi14_6h_class = 'table-success'
rsi14_6h_desc = 'Oversold (Buy)'
stochrsi14_15m_class = 'table-normal'
stochrsi14_15m_desc = 'Uneventful'
if df_6h_last['stochrsi14'].values[0] > 0.8:
stochrsi14_6h_class = 'table-danger'
stochrsi14_6h_desc = 'Overbought (Sell)'
elif df_6h_last['stochrsi14'].values[0] < 0.2:
stochrsi14_6h_class = 'table-success'
stochrsi14_6h_desc = 'Oversold (Buy)'
stochrsi14_1h_class = 'table-normal'
stochrsi14_1h_desc = 'Uneventful'
if df_1h_last['stochrsi14'].values[0] > 0.8:
stochrsi14_1h_class = 'table-danger'
stochrsi14_1h_desc = 'Overbought (Sell)'
elif df_1h_last['stochrsi14'].values[0] < 0.2:
stochrsi14_1h_class = 'table-success'
stochrsi14_1h_desc = 'Oversold (Buy)'
stochrsi14_6h_class = 'table-normal'
stochrsi14_6h_desc = 'Uneventful'
if df_6h_last['stochrsi14'].values[0] > 0.8:
stochrsi14_6h_class = 'table-danger'
stochrsi14_6h_desc = 'Overbought (Sell)'
elif df_6h_last['stochrsi14'].values[0] < 0.2:
stochrsi14_6h_class = 'table-success'
stochrsi14_6h_desc = 'Oversold (Buy)'
williamsr14_15m_class = 'table-normal'
williamsr14_15m_desc = 'Uneventful'
if df_15m_last['williamsr14'].values[0] > -20:
williamsr14_15m_class = 'table-danger'
williamsr14_15m_desc = 'Overbought (Sell)'
elif df_15m_last['williamsr14'].values[0] < -80:
williamsr14_15m_class = 'table-success'
williamsr14_15m_desc = 'Oversold (Buy)'
williamsr14_1h_class = 'table-normal'
williamsr14_1h_desc = 'Uneventful'
if df_1h_last['williamsr14'].values[0] > -20:
williamsr14_1h_class = 'table-danger'
williamsr14_1h_desc = 'Overbought (Sell)'
elif df_1h_last['williamsr14'].values[0] < -80:
williamsr14_1h_class = 'table-success'
williamsr14_1h_desc = 'Oversold (Buy)'
williamsr14_6h_class = 'table-normal'
williamsr14_6h_desc = 'Uneventful'
if df_6h_last['williamsr14'].values[0] > -20:
williamsr14_6h_class = 'table-danger'
williamsr14_6h_desc = 'Overbought (Sell)'
elif df_6h_last['williamsr14'].values[0] < -80:
williamsr14_6h_class = 'table-success'
williamsr14_6h_desc = 'Oversold (Buy)'
adx14_15m_class = 'table-normal'
adx14_15m_desc = 'Normal Trend'
if df_15m_last['adx14'].values[0] > 25 and df_15m_last['ema12'].values[0] >= df_15m_last['ema26'].values[0]:
adx14_15m_class = 'table-success'
adx14_15m_desc = 'Strong Trend Up'
elif df_15m_last['adx14'].values[0] > 25 and df_15m_last['ema12'].values[0] < df_15m_last['ema26'].values[0]:
adx14_15m_class = 'table-danger'
adx14_15m_desc = 'Strong Trend Down'
elif df_15m_last['adx14'].values[0] < 20 and df_15m_last['ema12'].values[0] >= df_15m_last['ema26'].values[0]:
adx14_15m_class = 'table-success'
adx14_15m_desc = 'Weak Trend Up'
elif df_15m_last['adx14'].values[0] < 20 and df_15m_last['ema12'].values[0] < df_15m_last['ema26'].values[0]:
adx14_15m_class = 'table-danger'
adx14_15m_desc = 'Weak Trend Up'
adx14_1h_class = 'table-normal'
adx14_1h_desc = 'Normal Trend'
if df_1h_last['adx14'].values[0] > 25 and df_1h_last['ema12'].values[0] >= df_1h_last['ema26'].values[0]:
adx14_1h_class = 'table-success'
adx14_1h_desc = 'Strong Trend Up'
elif df_1h_last['adx14'].values[0] > 25 and df_1h_last['ema12'].values[0] < df_1h_last['ema26'].values[0]:
adx14_1h_class = 'table-danger'
adx14_1h_desc = 'Strong Trend Down'
elif df_1h_last['adx14'].values[0] < 20 and df_1h_last['ema12'].values[0] >= df_1h_last['ema26'].values[0]:
adx14_1h_class = 'table-success'
adx14_1h_desc = 'Weak Trend Up'
elif df_1h_last['adx14'].values[0] < 20 and df_1h_last['ema12'].values[0] < df_1h_last['ema26'].values[0]:
adx14_1h_class = 'table-danger'
adx14_1h_desc = 'Weak Trend Up'
adx14_6h_class = 'table-normal'
adx14_6h_desc = 'Normal Trend'
if df_6h_last['adx14'].values[0] > 25 and df_6h_last['ema12'].values[0] >= df_6h_last['ema26'].values[0]:
adx14_6h_class = 'table-success'
adx14_6h_desc = 'Strong Trend Up'
elif df_6h_last['adx14'].values[0] > 25 and df_6h_last['ema12'].values[0] < df_6h_last['ema26'].values[0]:
adx14_6h_class = 'table-danger'
adx14_6h_desc = 'Strong Trend Down'
elif df_6h_last['adx14'].values[0] < 20 and df_6h_last['ema12'].values[0] >= df_6h_last['ema26'].values[0]:
adx14_6h_class = 'table-success'
adx14_6h_desc = 'Weak Trend Up'
elif df_6h_last['adx14'].values[0] < 20 and df_6h_last['ema12'].values[0] < df_6h_last['ema26'].values[0]:
adx14_6h_class = 'table-danger'
adx14_6h_desc = 'Weak Trend Up'
def arima_predictions(even_rows: bool = True):
results_ARIMA = ta.seasonalARIMAModel()
start_date = df_1h.last_valid_index()
end_date = start_date + datetime.timedelta(days=3)
arima_pred = results_ARIMA.predict(
start=str(start_date), end=str(end_date), dynamic=True
)
if even_rows:
arima_pred_rows = arima_pred.iloc[::2]
else:
arima_pred_rows = arima_pred.iloc[1::2]
html = ""
for index, pred in arima_pred_rows.iteritems():
html += f"""
<tbody>
<tr class={'table-success' if pred >= ticker[1] else 'table-danger'}>
<td>{index}</td>
<td>{pred}</td>
</tr>
</tbody>
"""
return html
return f"""